Dissertations / Theses on the topic 'Massive MIMO'

Les systèmes de communications MIMO utilisent des réseaux de capteurs qui peuvent s’étendre à de grandes dimensions (MIMO massifs) et qui sont pressentis comme solution potentielle pour les futurs standards de communications à très hauts débits. Un des problème majeur de ces systèmes est le fort niveau d’interférences dû au grand nombre d’émetteurs simultanés. Dans un tel contexte, les solutions ’classiques’ de conception de pilotes ’orthogonaux’ sont extrêmement coûteuses en débit utile permettant ainsi aux solutions d’identification de canal dites ’aveugles’ou ’semi-aveugles’ de revenir au-devant de la scène comme solutions intéressantes d’identification ou de déconvolution de ces canaux MIMO. Dans cette thèse, nous avons commencé par une analyse comparative des performances, en nous basant sur les CRB, afin de mesurer la réduction potentielle de la taille des séquences pilotes et ce en employant les méthodes dites semi-aveugles. Les résultats d’analyse montrent que nous pouvons réduire jusqu’à 95% des pilotes sans affecter les performances d’estimation du canal. Nous avons par la suite proposé de nouvelles méthodes d’estimation semi-aveugle du canal, permettant d’approcher la CRB. Nous avons proposé un estimateur semi-aveugle, LS-DF qui permet un bon compromis performance / complexité numérique. Un autre estimateur semi-aveugle de type sous-espace a aussi été proposé ainsi qu’un algorithme basé sur l’approche EM pour lequel trois versions à coût réduit ont été étudiées. Dans le cas d’un canal spéculaire, nous avons proposé un algorithme d’estimation paramétrique se basant sur l’estimation des temps d’arrivés combinée avec la technique DF
MIMO systems use sensor arrays that can be of large-scale (massive MIMO) and are seen as a potential candidate for future digital communications standards at very high throughput. A major problem of these systems is the high level of interference due to the large number of simultaneous transmitters. In such a context, ’conventional’ orthogonal pilot design solutions are expensive in terms of throughput, thus allowing for the so-called ’blind’ or ’semi-blind’ channel identification solutions to come back to the forefront as interesting solutions for identifying or deconvolving these MIMO channels. In this thesis, we started with a comparative performance analysis, based on CRB, to quantify the potential size reduction of the pilot sequences when using semi-blind methods that jointly exploit the pilots and data. Our analysis shows that, up to 95% of the pilot samples can be suppressed without affecting the channel estimation performance when such semi-blind solutions are considered. After that, we proposed new methods for semi-blind channel estimation, that allow to approach the CRB. At first, we have proposed a SB estimator, LS-DF which allows a good compromise between performance and numerical complexity. Other SB estimators have also been introduced based on the subspace technique and on the ML approach, respectively. The latter is optimized via an EM algorithm for which three reduced cost versions are proposed. In the case of a specular channel model, we considered a parametric estimation method based on times of arrival estimation combined with the DF technique

Next generation cellular wireless technology faces tough demands: increasing the throughput and reliability without consuming more resources, be it spectrum or energy. Massive mimo (Multiple-Input Multiple-Output) has proven, both in theory and practice, that it is up for the challenge. Massive mimo can offer uniformly good service to many users using low-end hardware, simultaneously, without increasing the radiated power compared to contemporary system. In Massive mimo, the base stations are equipped with hundreds of antennas. This abundance of antennas brings many new, interesting aspects compared to single-user mimo and multi-user mimo. Some issues of older technologies are nonexistent in massive mimo, while new issues in need of solutions arise. This thesis considers two aspects, and how these aspects differ in a massive mimo context: physical layer security and transmission of system information. First, it is shown that a jammer with a large number of antennas can outperform a traditional, single-antenna jammer in degrading the legitimate link. The excess of antennas gives the jammer opportunity to find and exploit structure in signals to improve its jamming capability. Second, for transmission of system information, the vast number of antennas prove useful even when the base station does not have any channel state information, because of the increased availability of space-time coding. We show how transmission without channel state information can be done in massive mimo by using a fixed precoding matrix to reduce the pilot overhead and simultaneously apply space-time block coding to use the excess of antennas for spatial diversity.
Det ställs hårda krav på nästa generations cellulära trådlösa system: att simultant öka datatakten på kommunikationen och dess tillförlitlighet utan att konsumera mer resurser, oavsett om det spektrum eller energi. Massiv mimo (eng: Multiple-Input Multiple-Output) har visat, både i teori och praktik, att tekniken är redo att tackla utmaningen. Massiv mimo kan betjäna många användare samtidigt, med god service, utan att öka den utstrålade effekten jämfört med nuvarande system. Massiv mimo, där basstationerna är utrustade med hundratals antenner, skiljer sig från dagens system vilket gör att många nya problem dyker upp och nya infallsvinklar på befintliga problem krävs. Denna avhandling analyserar två problem, och hur dessa förändras i ett massiv mimo sammanhang: säkerhet för fysiska lagret och överföring av systeminformation. Särskiljt visas att en störsändare med ett stort antal antenner kan överträffa en traditionell störsändare med en enda antenn. Antalet antenner ger störsändaren möjlighet att hitta strukturer i signaler och utnyttja detta för att förbättra störningens effekt. Det stora antalet antenner visar sig vara användbart även för överföring av systeminformation, där basstationen inte har någon kanalkännedom. Antennerna ger möjligheten att tillämpa spatial kodning (eng: space-time block coding). Vi visar hur överföringen utan kanalkännedom kan göras i massiv mimo genom att använda en fix förkodningsmatris för att reducera antalet pilotsymboler. Samtidigt kodar vi spatiellt över antennerna för att tillhandahålla spatiell diversitet.

To cover all the needs and requirements of mobile networks in the future, the predicted usage of the mobile networks has been split into three use-cases: enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine-type communication. In this thesis we focus on the massive machine-type communication use-case which is intended to facilitate the ever increasing number of smart devices and sensors. In the massive machine-type communication use-case, the main challenges are to accommodate a huge number of devices while keeping the battery lives of the devices long, and allowing them to be placed in far-away locations. However, these devices are not concerned about other features such as latency, high data rate, or mobility. In this thesis we study the application of massive MIMO (multiple-input multiple-output) technology for the massive machine-type communication use-case. Massive MIMO has been on the radar as an enabler for future communication networks in the last decade and is now firmly rooted in both academia and industry. The main idea of massive MIMO is to utilize a base station with a massive number of antennas which gives the ability to spatially direct signals and serve multiple devices in the same time- and frequency resource. More specifically, in this thesis we study A) a scenario where the base station takes advantage of a device's low mobility to improve its channel estimate, B) a random access scheme for massive machine-type communication which can accommodate a huge number of devices, and C) a case study where the benefits of massive MIMO for long range devices are quantified. The results are that the base station can significantly improve the channel estimates for a low mobility user such that it can tolerate lower SNR while still achieving the same rate. Additionally, the properties of massive MIMO greatly helps to detect users in random access scenarios and increase link-budgets compared to single-antenna base stations.

Massive multiple-input-multiple-output (MM) is becoming a promising candidate for wireless communications. The idea behind MM is to use a very large number of antennas to increase throughput and energy efficiency by one or more orders of magnitude. In order to make MM feasible, many challenges remain. In the uplink a fundamental question is whether to deploy single massive arrays or to build a virtual array using cooperative base stations. Also, in such large arrays the signal processing involved in receiver combining is non-trivial. Therefore, low complexity receiver designs and deployment scenarios are essential aspects of MM and the thesis mainly focuses on these two areas. In the first part, we investigate three deployment scenarios: (i) a massive co-located array at the cell center; (ii) a massive array clustered at B discrete locations; and (iii) a massive distributed array with a uniform distribution of individual antennae. We also study the effect of propagation parameters, system size, correlation and channel estimation error. We demonstrate by analysis and simulation that in the absence of any system imperfections, a massive distributed array is preferable. However, an intermediate deployment such as a massive array clustered at a few discrete locations can be more practical to implement and more robust to imperfect channel state information. We then focus on the performance of the co-located scenario with different types of antenna array, uniform square and linear arrays. With MM, it may be the case that large numbers of antennas are closely packed to fit in some available space. Hence, channel correlations become important and therefore we investigate the space requirements of different array shapes. In particular, we evaluate the system performance of uniform square and linear arrays by using ergodic capacity and capacity outage. For a range of correlation models, we demonstrate that the uniform square array can yield similar performance to a uniform linear array while providing considerable space saving. In the second part of the thesis we focus on low complexity receiver designs. Due to the high dimension of MM systems there is a considerable interest in detection schemes with a better complexity-performance trade-off. We focus on linear receivers (zero forcing (ZF) and maximum ratio combining (MRC)) used in conjuction with a Vertical Bell Laboratories Layered Space Time (V-BLAST) structure. Our first results show that the performance of MRC V-BLAST approaches that of ZF V-BLAST under a range of imperfect CSI levels, different channel powers and different types of arrays as long as the channel correlations are not too high. Subsequently, we propose novel low complexity receiver designs which maintain the same performance as ZF or ZF V-BLAST. We show that the performance loss of MRC relative to ZF can be removed in certain situations through the use of V-BLAST. The low complexity ordering scheme based on the channel norm (C-V-BLAST) results in a V-BLAST scheme with MRC that has much less complexity than a single ZF linear combiner. An analysis of the SINR at each stage of the V-BLAST approach is also given to support the findings of the proposed technique. We also show that C-V-BLAST remains similar to ZF for more complex adaptive modulation systems and in the presence of channel estimation error, C-V-BLAST can be superior. These results are analytically justified and we derive an exhaustive search algorithm for power control (PC) to bound the potential gains of PC. Using this bound, we demonstrate that C-V-BLAST performs well without the need for additional PC. The final simplification is based on the idea of ordering users based on large scale fading information rather than instantaneous channel knowledge for a V-BLAST scheme with MRC (P-V-BLAST). An explicit closed form analysis for error probability for both co-located and distributed BSs is provided along with a number of novel performance metrics which are useful in designing MM systems. It is shown that the error performance of the distributed scenario can be well approximated by a modified version of a co-located scenario. Another potential advantage of P-V-BLAST is that the ordering can be obtained as soon as the link gains are available. Hence, it is possible that mean SINR values could be used for scheduling and other link control functions. These mean values are solely functions of the link gains and hence, scheduling, power adaptation, rate adaptation, etc. can all be performed more rapidly with P-V-BLAST. Hence, the P-V-BLAST structure may have further advantages beyond a lower complexity compared to C-V-BLAST.

Half-Duplex Multiuser Multiple-Input Multiple-Output (HD MU-MIMO) systemscurrently employed in communication systems are not experiencing the selfinterference(SI) problem but they are not optimal in terms of efficiency and interms of resources used (time and frequency resources). Ignoring the effect of largescalefading, we start by explaining the uplink (UL) and downlink (DL) parts ofthe MU-MIMO system and how the sum-rate is calculated. We also introduce thethree linear receivers/precoders, Maximum-Ratio Combining (MRC)/Maximum-Ratio Transmission (MRT), Zero-Forcing (ZF), and Minimum Mean-Square Error(MMSE) and which of the three types is going to be used in the study of Full-Duplex Multiuser Multiple-input Multiple-output (FD MU-MIMO) system. Thenwe introduce FD MU-MIMO system, and how the equation used to calculate thesum-rate of the UL part changes when the SI occurs, and why SI problem is notpresent in the DL part. Next, we introduce the spectral efficiency (SE), and howto calculate it and why it is taken as a parameter to compare HD and FD systems.Also the effect of SI on FD MU-MIMO system is presented through simulationgraphs, then we move to show how to reduce SI effect by increasing the number ofantennas in the base-station (BS). Lastly, we take the effect of large scale fading inorder to reach a simple statistical model in the form cumulative distribution function(CDF) graph for different values of SI and compare those of FD MU-MIMOsystem to HD MU-MIMO. The results show that FD MU-MIMO together withmassive MIMO technology is very promising and would save time and frequencyresources which means an increase in the SE but SI must be below a certain level.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
Este trabalho de dissertação de mestrado apresenta uma comparação de algumas das técnicas de detecção de sinais mais promissoras para a viabilização de sistemas MIMO de grande porte em termos de desempenho, taxa de erro de bit e complexidade, número médio de flops requeridos por vetor de símbolos recebido. Com este objetivo foram também consideradas as técnicas de detecção clássicas, visando assim ressaltar o desempenho das novas técnicas com relação as antigas. Além disso foram propostas e investigadas novas estruturas para detectores SIC baseados em lista (i.e., com múltiplos ramos) que resultaram em melhor desempenho com menor complexidade quando comparados aos detectores deste tipo já propostos. Na comparação dos algoritmos, foram considerados três cenários diferentes: (i ) monousuário, com ganhos de canal gaussianos complexos independentes e identicamente distribuídos, ou seja, uma propagação que só considera a presença de desvanecimento de Rayleigh; (ii ) múltiplos usuários com canais correlatados e que considera as perdas de propagação de pequena e larga escala num sistema com antena centralizada; e (iii ) múltiplos usuários com canais correlatados e que considera as perdas de propagação de pequena e larga escala num sistema com antena distribuída.
This work dissertation presents a comparison of some of the signal detection techniques most promising for the viability of large MIMO systems in terms of performance, bit error rate, and complexity, average number of flops required by transmitted symbol vector. For this purpose it was also considered classical detection techniques, thus aiming to highlight the performance of new techniques with respect the old. Also it has been proposed and investigated new structures to SIC detectors based on list (i.e., with multiple branches) resulting in better performance with less complexity compared to detectors of this kind already proposed. In the comparison of algorithms, three different scenarios were used: (i ) single user, with channel gains independent and distributed identically complex Gaussian, that is, a spread that only considers the presence of Rayleigh fading; (ii ) multiple users, with correlated channels, and considers the short and large scale path loss in a system with centralized antenna; e (iii ) multiple users, with correlated channels, and considers the short and large scale path loss in a system with distributed antenna.

Massive multiple-input multiple-output (MIMO) technology is considered as one of the enabling technologies to scale up the data rates for the future communication systems. Traditional MIMO systems employ digital beamforming where each antenna element is equipped with one radio frequency (RF) chain. When the number of the antennas are scaled up, the cost and power consumption of massive MIMO systems also increase significantly. Recently, hybrid analog-and-digital beamformers have attracted a lot of attention as a cost effective approach to benefit from the advantages of massive MIMO. In hybrid structure, a small number of RF chains are connected to a large number of antennas through a network of phase shifters. The optimal hybrid beamforming problem is a complex nonconvex optimization due to the nonconvex constraint imposed by phase shifters. The overall objective of this thesis is to provide simple and effective hybrid beamforming solutions for narrowband point-to-point and multiuser massive MIMO scenarios. Firstly, hybrid beamforming problem for a point-to-point communication system with perfect channel state information (CSI) is investigated, and an effective codebook based hybrid beamforming with low resolution phase shifters is proposed which is suitable for sparse scattering channels. Then, by leveraging the properties of massive MIMO, an asymptotically optimal hybrid beamforming solution as well as its closed-form formula will be presented. It will be shown that the proposed method is effective in both sparse and rich scattering propagation environments. In addition, the closed-form expression and lower-bounds for the achievable rates are derived when analog and digital phase shifters are employed. Secondly, hybrid beamforming problem to maximise the total sum-rate for the downlink of multiuser MIMO is investigated, and an effective solution as well as its closed-form expression for this system is proposed. The presented solutions for the single-antenna and multiantenna scenarios are shown to be effective as they can achieve a similar sum-rate as digital beamforming can reach. In addition, it is shown that the proposed technique with low-cost low resolution phase shifters at the RF beamformer demonstrates a comparable performance to that of a hybrid beamformer with an expensive analog beamformer. Finally, two novel hybrid beamforming techniques are proposed to reduce the power consumption at the RF beamformer. Defining a threshold level, it is shown that half of the phase shifters could be turned off without a performance loss when the wireless channel matrix is modeled by Rayleigh fading. Then, we reduce the number of the phase shifters by using a combination of phase shifters and switches at the RF beamformer. The proposed methods can significantly reduce the power consumption as switches, in general, have lower power consumption compared to phase shifters. It is noted that the presented algorithms and the closed-form expressions of their performance are derived by using the asymptotic properties of the elements of the singular vectors for the rich scattering channel matrix.

The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: i) having a high throughput; ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource,  can meet the above requirements, and hence, it is a promising candidate technology for next generations of wireless systems. With massive antenna arrays at the BS, for most propagation environments, the channels become favorable, i.e., the channel vectors between the users and the BS are (nearly) pairwisely orthogonal, and hence, linear processing is nearly optimal. A huge throughput and energy efficiency can be achieved due to the multiplexing gain and the array gain. In particular, with a simple power control scheme, Massive MIMO can offer uniformly good service for all users. In this dissertation, we focus on the performance of Massive MIMO. The dissertation consists of two main parts: fundamentals and system designs of Massive MIMO. In the first part, we focus on fundamental limits of the system performance under practical constraints such as low complexity processing, limited length of each coherence interval, intercell interference, and finite-dimensional channels. We first study the potential for power savings of the Massive MIMO uplink with maximum-ratio combining (MRC), zero-forcing, and minimum mean-square error receivers, under perfect and imperfect channels. The energy and spectral efficiency tradeoff is investigated. Secondly, we consider a physical channel model where the angular domain is divided into a finite number of distinct directions. A lower bound on the capacity is derived, and the effect of pilot contamination in this finite-dimensional channel model is analyzed. Finally, some aspects of favorable propagation in Massive MIMO under Rayleigh fading and line-of-sight (LoS) channels are investigated. We show that both Rayleigh fading and LoS environments offer favorable propagation. In the second part, based on the fundamental analysis in the first part, we propose some system designs for Massive MIMO. The acquisition of channel state information (CSI) is very importantin Massive MIMO. Typically, the channels are estimated at the BS through uplink training. Owing to the limited length of the coherence interval, the system performance is limited by pilot contamination. To reduce the pilot contamination effect, we propose an eigenvalue-decomposition-based scheme to estimate the channel directly from the received data. The proposed scheme results in better performance compared with the conventional training schemes due to the reduced pilot contamination. Another important issue of CSI acquisition in Massive MIMO is how to acquire CSI at the users. To address this issue, we propose two channel estimation schemes at the users: i) a downlink "beamforming training" scheme, and ii) a method for blind estimation of the effective downlink channel gains. In both schemes, the channel estimation overhead is independent of the number of BS antennas. We also derive the optimal pilot and data powers as well as the training duration allocation to maximize the sum spectral efficiency of the Massive MIMO uplink with MRC receivers, for a given total energy budget spent in a coherence interval. Finally, applications of Massive MIMO in relay channels are proposed and analyzed. Specifically, we consider multipair relaying systems where many sources simultaneously communicate with many destinations in the same time-frequency resource with the help of a massive MIMO relay. A massive MIMO relay is equipped with many collocated or distributed antennas. We consider different duplexing modes (full-duplex and half-duplex) and different relaying protocols (amplify-and-forward, decode-and-forward, two-way relaying, and one-way relaying) at the relay. The potential benefits of massive MIMO technology in these relaying systems are explored in terms of spectral efficiency and power efficiency.

Mobile data tra_c is predicted to grow 1000x from now until 2030 [1, 2], and dense small cell networks (SCNs) and massive multiple input multiple output (mMIMO) are considered the major pilar technologies to meet this ever-increasing capacity demand in the years to come. Dense SCNs which is comprised of picocells, femtocells, metrocells, etc are considered to be one of the main approaches to signi_cantly increase the network capacity and meet the capacity demand in 5G network. Indeed, the orthogonal deployment of SCNs with existing macrocells has already been applied as one solution in 4thgeneration and 5-th generation networks by the 3rd Generation Partnership Project (3GPP). SCNs are able to enhance the network capacity because of the high spatial reuse, eg, network capacity could potentially grow linearly with the number of small cells. In this thesis, We _rst discuss the performance analysis of dense SCNs using stochastic geometry. On the other hand, massive multiple input and multiple output (mMIMO) is also considered as one of the most important candidate technologies to meet the everincreasing capacity demand in the years to come [2]. By exploiting its many antennas and thus degrees of freedom in the spatial domain, mMIMO can increase the per-cell and the area spectral e_ciency (ASE) through spatial multiplexing. The larger the number of antennas, the larger the number of degrees of freedom, and thus the more multiplexing opportunities. However, when a time division duplex (TDD) system is considered, the performance of mMIMO may be limited by inaccurate channel state information (CSI). Pilot contamination is one of the major bottlenecks, and occurs when the same set of uplink (UL) pilot sequences is reused across neighbouring cells. Other channel estimation impairments also play a role. In this thesis, we _rstly introduce the performance evaluation of dense SCNs using stochastic. Previously, performance analysis of cellular networks is under the assumption that the base stations(BSs) and user equipments(UEs) are placed randomly or deterministically. The models under these assumptions are highly idealized. Stochastic geometry is introduced as a very tractable approach to analyze the networks performance of cellular networks. However, it is notable that the results are base on considerable simpli_cation on network scenarios. In this thesis, more realworld features of SCNs are considered. The BSs are activated when there is UE connected to them which is called Idle Mode. moreover, Both piece-wise path loss function and probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) transmission are further considered. The analysis demonstrates that when the activated BS density is larger than a threshold, the coverage is su_er a decrease, which results in a slow growth or a decrease in ASE. Since LoS and NLoS transmission are considered, the probability of an interference path changing from NLoS to LoS becomes larger. As a result, the deployment of SCNs should be paid more attention as increasing BS density will probably lead to a small improvement of network performance or even a worse result. In addition to dense SCNs, mMIMO, considered as a scaled-up version of multiuser MIMO (MU-MIMO), it is important to note that the larger the number of antennas, the larger the number of degrees of freedom, and thus the more multiplexing opportunities. However, when time division duplex (TDD) systems are considered, due to a _nite channel coherent time, the performance of mMIMO systems may be limited by inaccurate channel state information (CSI). Pilot contamination is considered as a major bottleneck, which occurs when the same set of uplink training sequences is reused across neighbouring cells [3]. Other channel estimation impairments also play a role. In this thesis, we conduct performance analysis for uplink (UL) massive multiple input and multiple output (mMIMO) networks using stochastic geometry. With the consideration of practical system assumptions, such as sophisticated path loss model incorporating both LoS and NLoS transmissions and a _nite user equipment (UE) density, we derive the coverage probability and the area spectral e_ciency (ASE) performance.

Ao longo deste trabalho, problemas relacionados aos sistemas de comunicação equipados com múltiplas antenas no transmissor e receptor (MIMO - Multiple- Input Multiple-Output) são analisados sob o ponto de vista de detecção clássica, da otimização não-linear, bem como da pré-codificação linear, desde MIMO convencional (algumas antenas no Tx e Rx) até sistemas MIMO de larga-escala (massivo). Inicialmente, a eficiência de detecção de vários detectores MIMO foi analisada sob a prerrogativa de canais altamente correlacionados, situação em que sistemas MIMO apresentam elevada perda de desempenho, além de, em alguns casos, uma crescente complexidade. Diante deste cenário, foi estudado especificamente o comportamento em termos do compromisso complexidade x taxa de erro de bits (BER - Bit Error Rate), para diferentes técnicas de detecção, como o cancelamento de interferências sucessivo (SIC), redução treliça (LR), bem como a combinação de cada uma destas às técnicas lineares de detecção. Nessa análise, também foram considerados diferentes estruturas de antenas uniformes com arranjos geométricos lineares (ULA - uniform linear array) e de arranjo planar (UPA - uniform planar array) em ambos transmissor e receptor. Além disso, também foram considerados diferentes número de antenas e ordem de modulação. Em seguida, o problema de detecção MIMO foi estudado sob uma perspectiva de otimização não-linear, visando especificamente alcançar o desempenho ótimo. Foi analisada a solução de detecção com relaxação semi-definida (SDR - semi- definite relaxation). O detector SDR-MIMO é uma abordagem eficiente capaz de atingir o desempenho muito próximo ao ótimo, especialmente para baixas e médias ordens de modulação. Concentramos nossos esforços no desenvolvimento de uma aproximação computacionalmente eficiente para o algoritmo de detecção de máxima verossimilhança (ML - Maximum Likelihood) MIMO baseado na programação semi-definida (SDP - Semidefinite Programming) para as constelações M-QAM. Finalmente, estuda-se um problema de alocação de potência com o objetivo de maximizar a capacidade de um canal de broadcasting MIMO massivo em uma única célula equipada com pré-codificação forçagem à zero (ZFBF - zero-forcing beamforming) e inversão de canal regularizado (RCI - regularized channel inversion) na estação rádio base (BS). Nosso objetivo é investigar esse problema considerando um sistema massivo no limite, ou seja, quando o número de usuários, K, e antenas na BS, M, tendem ao infinito porém com uma razão constante, β = K M . Primeiramente deriva-se a relação sinal-interferência mais ruído (SINR) para ambos os pré-codificadores escolhidos. Em seguida, investiga-se um esquemas de alocação de potência ótimo que maximiza a soma das capacidades por antena sob uma restrição de potência máxima disponível, conclui-se que o problema é convexo e que a alocação de potência ótima segue a estratégia de watter-filling (WF). Também estudou-se o problema relacionado à alocação de potência em um grupo finito de usuários separados em grupos e determinou-se o impacto desse esquema na capacidade total do sistema.
Throughout this work, problems related to communication systems equipped with multiple antennas in the transmitter and receiver (MIMO - Multiple-Input Multiple-Output) are analyzed from the point of view of classical detection, nonlinear optimization, as well as linear pre-coding, from conventional MIMO (some Tx and Rx antennas) to large-scale (massive) MIMO systems. Initially, the detection efficiency of several MIMO detectors were analyzed under the prerogative of highly correlated channels, in which situation, MIMO systems present a high loss of performance, and, in some cases, an increasing complexity. Considering this scenario, we have specifically studied the behavior in terms of compromise complexity x bit error rate (BER), for different detection techniques, such as the successive interference cancellation (SIC), lattice reduction (LR), as well as the combination of each of these with linear detection techniques. In this analysis, different uniform antenna structures with uniform linear array (ULA) and planar array array (UPA) were also considered in both transmitter and receiver side. In addition, different number of antennas and order of modulation were also considered. Next, the MIMO detection problem was studied from a nonlinear optimization perspective, specifically aiming to achieve optimum performance. The detection solution with semi-defined relaxation (SDR - it semidefinite relaxation) were analyzed. The SDR-MIMO detector is an efficient approach capable of achieving near-optimal performance, especially for low and medium modulation orders. We focused our efforts on developing a computationally efficient approach for the maximum likelihood (ML) MIMO detection algorithm based on semi-definite programming (SDP) for M-QAM constellations. Finally, we study an optimal power allocation problem aiming to maximizes the sum-rate capacity of a single cell massive MIMO broadcast channel equipped with zero-forcing beamforming (ZFBF) and regularized channel inversion (RCI) precoding at the base station (BS). Our purpose is to investigate this problem in the large-scale system limit, i.e, when the number of users, K, and antennas at the BS, M, tend to infinity with a ratio β = K/M being held constant. We first derive the signal to interference plus noise (SINR) ratio for both chosen precoders. Then we investigate optimal power allocation schemes that maximize the sum-rate per antenna under an average power constraint and we show that the problem is convex and the power allocation follows the well-known Water-Filling strategy. We also studied a problem related to an optimal power allocation at a finite group of clustered users and determine the impact of this scheme in the ergodic sum-rate capacity.

The 5th generation mobile telecommunication system (5G) is currently being specified and developed, with large expectations on throughput and efficiency. While 4G and more specifically LTE might constitute a basis of the design of the network, there are some parts that should be improved. One thing to improve is the static signalling that occurs very frequently in a 4G network, of which system information such as synchronization signals, detection of network frequencies, operators, configurations etc. is a part. It has been shown that the static signalling requires both much energy and time-frequency resources. Since the system information is not intended for a single user it is always broadcast so that any user, and any amount of users can read it when needed. 5G will use a technique called massive MIMO, where the base station is equipped with a large number of antennas which can be used to direct signals in space, called beamforming. This thesis presents a new method for distribution of system information that can utilize the beamforming capabilities of massive MIMO. A simple model together with simulated user channel statistics from urban 4G scenarios are used to show that the new method outperforms the classical method of only broadcasting the information, with respect to time-frequency resources. Especially if there are high requirements on the latency of the system information, the new method results in a large gain.

Massive multiple-input multiple-output (MIMO) is one of the key technologies for the emerging fifth generation (5G) wireless networks, and has the potential to tremendously improve spectral and energy efficiency with low-cost implementations. While massive MIMO systems have drawn great attention from both academia and industry, few efforts have been made on how the richness of the spatial dimensions offered by massive MIMO affects wireless security. As security is crucial in all wireless systems due to the broadcast nature of the wireless medium, in this thesis, we study how massive MIMO technology can be used to guarantee communication security in the presence of a passive multi-antenna eavesdropper. Our proposed massive MIMO system model incorporates relevant design choices and constraints such as time-division duplex (TDD), uplink training, pilot contamination, low-complexity signal processing, and low-cost hardware components. The thesis consists of three main parts. We first consider physical layer security for a massive MIMO system employing simple artificial noise (AN)-aided matched-filter (MF) precoding at the base station (BS). For both cases of perfect training and pilot contamination, we derive a tight analytical lower bound for the achievable ergodic secrecy rate, and an upper bound for the secrecy outage probability. Both bounds are expressed in closed form, providing an explicit relationship between all system parameters, offering significant insights for system design. We then generalize the work by comparing different types of linear data and AN precoders in a secure massive MIMO network. The system performance, in terms of the achievable ergodic secrecy rate is obtained in closed form. In addition, we propose a novel low-complexity data and AN precoding strategy based on a matrix polynomial expansion. Finally, we consider a more realistic system model by taking into account non-ideal hardware components. Based on a general hardware impairment model, we derive a lower bound for the ergodic secrecy rate achieved by each user when AN-aided MF precoding is employed at the BS. By exploiting the derived analytical bound, we investigate the impact of various system parameters on the secrecy rate and optimize both the uplink training pilots and AN precoder to maximize the secrecy rate.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

This thesis addresses and identifies outstanding challenges associated with the Multi user massive Multiple-Input Multiple-Output (MU massive MIMO) transmission, whereby various system scenarios have been considered to tackle these challenges. First, for a single cell scenario, the uplink effective capacity under statistical exponent constraints, the asymptotic error and outage probabilities in a multi user massive MIMO system are provided. The proposed approach establishes closed form expressions for the aforementioned metrics under both perfect and imperfect channel state information (CSI) scenarios. In addition, expressions for the asymptotically high signal-to-interference ratio (SIR) regimes are established. Second, the statistical queueing constraints, pilot contamination phenomenon and fractional power control in random or irregular cellular massive MIMO system are investigated, where base station locations are modelled based on the Poisson point process. Specifically, tractable analytical expressions are developed for the asymptotic SIR coverage, rate coverage and the effective capacity under the quality of service statistical exponent constraint. Laplace transform of interference is derived with the aid of mathematical tools from stochastic geometry. Simulation outcomes demonstrate that pilot reuse impairments can be alleviated by employing a cellular frequency reuse scheme. For example, with unity frequency reuse factor, we see that 40% of the total users have SIR above −10.5dB, whereas, with a reuse factor of 7, the same fraction of users have SIR above 20.5dB. In addition, for a certain parameters setting, the coverage probability in the lower 50th percentile can be maximized by adjusting power compensation fraction between 0.2 and 0.5. Also, for SIR threshold of 0dB, allocating 0.25 fraction of uplink transmit power can achieve approximately 6% improvement in coverage probability in the cell edge area compared to constant power policy and about 14% improvement compared to the full channel-inversion policy. Third and last, motivated by the powerful gains of incorporating small cells with macro cells, a massive MIMO aided heterogeneous cloud radio access network (H-CRAN) is investigated. More specific, based on Toeplitz matrix tool, tractable formulas for the link reliability and rate coverage of a typical user in H-CRAN are derived. Numerical outcomes confirm the powerful gain of the massive MIMO for enhancing the throughput of the H-CRAN while small remote radio heads (RRH cells) are capable of achieving higher energy efficiency.

In massive multiple input multiple output (MIMO) systems, major challenges are present due to the large number of active antennas and radio frequency (RF) chains,suchasincreasedpowerconsumptionandcomputationcomplexity. Transmitantennaselection(TAS)isbeinginvestigatedasasolutiontotacklethesechallenges. In this thesis, a dynamic transmit antenna selection technique is proposed whichcanmaximizethesumrateofamultiuser(MU)-MIMOcommunicationsystem. In order to satisfy the objective, the number of transmit antennas required is determined by remodeling it as a binary Knapsack Problem (KP) and then extending to a Multiple KP (MKP) for MU-MIMO. Furthermore, an improvement in the decision making is also proposed with the introduction of a flexible decision criteria, whilst reducing the structure of the MKP to resemble that of a single binary KP. Additionally, comparisons of the KP based algorithms are done with two low complexity techniques, which are the sequential selection algorithm and random selection algorithm. Results show that the KP based techniques outperform these low complexity techniques. The modified binary KP algorithm is also superior to that of the MKP, as it is not sensitive to solving as binary knapsack sub-problems. The proposed technique has good performance for different antenna selection measures and is suitable to ensure communication efficiency in future wireless communication systems.

Owing to its ability to improve both spectral and energy efficiency of wireless networks, massive multiple-input multiple-output (mMIMO) has become one of the key enablers of the fifth-generation (5G) and beyond communication systems. For successful integration of this promising physical layer technique in the upcoming cellular standards, it is essential to have a comprehensive understanding of its network-level performance. Over the last decade, stochastic geometry has been instrumental in obtaining useful system design insights of wireless networks through accurate and tractable theoretical analysis. Hence, it is only natural to consider modeling and analyzing the mMIMO systems using appropriate statistical constructs from the stochastic geometry literature and gain insights for its future implementation. With this broader objective in mind, we first focus on modeling a cellular mMIMO network that uses fractional pilot reuse to mitigate the sole performance-limiting factor of mMIMO networks, namely, pilot contamination. Leveraging constructs from the stochastic geometry literature, such as Johnson-Mehl cells, we derive analytical expressions for the uplink (UL) signal-to-interference-and-noise ratio (SINR) coverage probability and average spectral efficiency for a random user. From our system analysis, we present a partitioning rule for the number of pilot sequences to be reserved for the cell-center and cell-edge users that improves the average cell-edge user spectral efficiency while achieving similar cell-center user spectral efficiency with respect to unity pilot reuse. In addition, using the analytical approach developed for the cell-center user performance evaluation, we study the performance of a small cell system where user and base station (BS) locations are coupled. The impact of distance-dependent UL power control on the performance of an mMIMO network with unity pilot reuse is analyzed and subsequent system design guidelines are also presented. Next, we focus on the performance analysis of the cell-free mMIMO network, which is a distributed implementation of the mMIMO system that leads to the second and third contributions of this dissertation. Similar to the cellular counterpart, the cell-free systems also suffer from pilot contamination due to the reuse of pilot sequences throughout the network. Inspired by a hardcore point process known as the random sequential adsorption (RSA) process, we develop a new distributed pilot assignment algorithm that mitigates the effect of pilot contamination by ensuring a minimum distance among the co-pilot users. This pilot assignment scheme leads to the construction of a new point process, namely the multilayer RSA process. We study the statistical properties of this point process both in one and two-dimensional spaces by deriving approximate but accurate expressions for the density and pair correlation functions. Leveraging these new results, for a cell-free network with the proposed RSA-based pilot assignment scheme, we present an analytical approach that determines the minimum number of pilots required to schedule a user with probabilistic guarantees. In addition, to benchmark the performance of the RSA-based scheme, we propose two optimization-based centralized pilot allocation schemes using linear programming principles. Through extensive numerical simulations, we validate the efficacy of the distributed and scalable RSA-based pilot assignment scheme compared to the proposed centralized algorithms. Apart from pilot contamination, another impediment to the performance of a cell-free mMIMO is limited fronthaul capacity between the baseband unit and the access points (APs). In our fourth contribution, using appropriate stochastic geometry-based tools, we model and analyze the downlink of such a network for two different implementation scenarios. In the first scenario, we consider a finite network where each AP serves all the users in the network. In the second scenario, we consider an infinite network where each user is served by a few nearby APs in order to limit the load on fronthaul links. From our analyses, we observe that for the finite network, the achievable average system sum-rate is a strictly quasi-concave function of the number of users in the network, which serves as a key guideline for scheduler design for such systems. Further, for the user-centric architecture, we observe that there exists an optimal number of serving APs that maximizes the average user rate. The fifth and final contribution of this dissertation focuses on the potential improvement that is possible by the use of mMIMO in citizen broadband radio service (CBRS) spectrum sharing systems. As a first concrete step, we present comprehensive modeling and analysis of this system with omni-directional transmissions. Our model takes into account the key guidelines by the Federal Communications Commission for co-existence between licensed and unlicensed networks in the 3.5 GHz CBRS frequency band. Leveraging the properties of the Poisson hole process and Matern hardcore point process of type II, a.k.a. ghost RSA process, we analytically characterize the impact of different system parameters on various performance metrics such as medium access probability, coverage probability, and area spectral efficiency. Further, we provide useful system design guidelines for successful co-existence between these networks. Building upon this omni-directional model, we also characterize the performance benefits of using mMIMO in such a spectrum sharing network.
Doctor of Philosophy
The emergence of cloud-based video and audio streaming services, online gaming platforms, instantaneous sharing of multimedia contents (e.g., photos, videos) through social networking platforms, and virtual collaborative workspace/meetings require the cellular communication networks to provide high data-rate as well as reliable and ubiquitous connectivity. These constantly evolving requirements can be met by designing a wireless network that harmoniously exploits the symbiotic co-existence among different types of cutting-edge wireless technologies. One such technology is massive multiple-input multiple-output (mMIMO), whose core idea is to equip the cellular base stations (BSs) with a large number of antennas that can be leveraged through appropriate signal processing algorithms to simultaneously accommodate multiple users with reduced network interference. For successful deployment of mMIMO in the upcoming cellular standards, i.e., fifth-generation (5G) and beyond systems, it is necessary to characterize its performance in a large-scale wireless network taking into account the inherent spatial randomness in the BS and user locations. To achieve this goal, in this dissertation, we propose different statistical methods for the performance analysis of mMIMO networks using tools from stochastic geometry, which is a field of mathematics related to the study of random patterns of points. One of the major deployment issues of mMIMO systems is pilot contamination, which is a form of coherent network interference that degrades user performance. The main reason behind pilot contamination is the reuse of pilot sequences, which are a finite number of known signal waveforms used for channel estimation between a user and its serving BS. Further, the effect of pilot contamination is more severe for the cell-edge users, which are farther from their own BSs. An efficient scheme to mitigate the effect of pilot contamination is fractional pilot reuse (FPR). However, the efficiency of this scheme depends on the pilot partitioning rule that decides the fraction of total pilot sequences that should be used by the cell-edge users. Using appropriate statistical constructs from the stochastic geometry literature, such as Johnson-Mehl cells, we present a partitioning rule for efficient implementation of the FPR scheme in a cellular mMIMO network. Next, we focus on the performance analysis of the cell-free mMIMO network. In contrast to the cellular network, where each user is served by a single BS, in a cell-free network each user can be served by multiple access points (APs), which have less complex hardware compared to a BS. Owing to this cooperative and distributed implementation, there are no cell-edge users. Similar to the cellular counterpart, the cell-free systems also suffer from pilot contamination due to the reuse of pilot sequences throughout the network. Inspired by a hardcore point process known as the random sequential adsorption (RSA) process, we develop a new distributed pilot assignment algorithm that mitigates the effect of pilot contamination by ensuring a minimum distance among the co-pilot users. Further, we show that the performance of this distributed pilot assignment scheme is appreciable compared to different centralized pilot assignment schemes, which are algorithmically more complex and difficult to implement in a network. Moreover, this pilot assignment scheme leads to the construction of a new point process, namely the multilayer RSA process. We derive the statistical properties of this point process both in one and two-dimensional spaces. Further, in a cell-free mMIMO network, the APs are connected to a centralized baseband unit (BBU) that performs the bulk of the signal processing operations through finite capacity links, such as fiber optic cables. Apart from pilot contamination, another implementational issue associated with the cell-free mMIMO systems is the finite capacity of fronthaul links that results in user performance degradation. Using appropriate stochastic geometry-based tools, we model and analyze this network for two different implementation scenarios. In the first scenario, we consider a finite network where each AP serves all the users in the network. In the second scenario, we consider an infinite network where each user is served by a few nearby APs. As a consequence of this user-centric implementation, for each user, the BBU only needs to communicate with fewer APs thereby reducing information load on fronthaul links. From our analyses, we propose key guidelines for the deployment of both types of scenarios. The type of mMIMO systems that are discussed in this work will be operated in the sub-6 GHz frequency range of the electromagnetic spectrum. Owing to the limited availability of spectrum resources, usually, spectrum sharing is encouraged among different cellular operators in such bands. One such example is the citizen broadband radio service (CBRS) spectrum sharing systems proposed by the Federal Communications Commission (FCC). The final contribution of this dissertation focuses on the potential improvement that is possible by the use of mMIMO in the CBRS systems. As our first step, using tools from stochastic geometry, we model and analyze this system with a single antenna at the BSs. In our model, we take into account the key guidelines by the FCC for co-existence between licensed and unlicensed operators. Leveraging properties of the Poisson hole process and hardcore process, we provide useful theoretical expressions for different performance metrics such as medium access probability, coverage probability, and area spectral efficiency. These results are used to obtain system design guidelines for successful co-existence between these networks. We further highlight the potential improvement in the user performance with multiple antennas at the unlicensed BS.

Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 55-58).
Massive Multiple-Input Multiple-Output (MIMO) is a key enabler for fifth generation (5G) cellular communication systems. Massive MIMO gives rise to challenging signal detection problems for which traditional detectors are either impractical or suffer from performance limitations. Recent work has proposed several learning approaches to MIMO detection with promising results on simple channel models (e.g., i.i.d. Gaussian entries). However, we find that the performance of these schemes degrades significantly in real-world scenarios in which the channels of different receivers are spatially correlated. The root of this poor performance is that these schemes either do not exploit the problem structure (requiring models with millions of training parameters), or are overly-constrained to mimic algorithms that require very specific assumptions about the channel matrix. We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channel matrices with the same or lower computational complexity. MMNet's design builds on the theory of iterative soft-thresholding algorithms to identify the right degree of model complexity, and it uses a novel training algorithm that leverages temporal and frequency locality of channel matrices at a receiver to accelerate training. Together, these innovations allow MMNet to train online for every realization of the channel. On i.i.d. Gaussian channels, MMNet requires 2 orders of magnitude fewer operations than existing deep learning schemes but achieves near-optimal performance. On spatially-correlated realistic channels, MMNet achieves the same error rate as the next-best learning scheme (OAMPNet [1]) at 2.5dB lower Signal-to-Noise Ratio (SNR) and with at least lOx less computational complexity. MMNet is also 4-8dB better overall than a classic linear scheme like the minimum mean square error (MMSE) detector.
by Mehrdad Khani Shirkoohi.
S.M. in Computer Science and Engineering
S.M.inComputerScienceandEngineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the frequency bandwidth for cellular communication is limited, significant effort was dedicated to improve the utilization of the available spectrum and increase the system performance via new technologies. For example, 3G and 4G networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is a necessity for new cellular network technologies to accommodate the ever-growing data traffic demand. 5G is behind the corner to deal with the tremendous data traffic requirements that will appear in cellular networks in the next decade. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. As an outcome of using Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to properly allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods to deliver high spectral and energy efficiency of Massive MIMO networks. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. In the first part of this thesis, we investigate reusing the resources of a Massive MIMO system, for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO systems by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario in comparison with conventional Massive MIMO systems to limit the interference that is caused between the cellular network and the D2D communication, thereby enabling their coexistence. In this part, we propose a novel pilot transmission scheme for D2D users to limit the interference to the channel estimation phase of cellular users in comparison with the case of sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO systems. This method aims at assuring that D2D communication enhances the SE of the network in comparison with conventional Massive MIMO systems. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO systems. The new power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, which are based on the network-wide max-min and proportional fairness performance metrics. We first explain the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve this optimization problem, we prove that it can be rewritten in a convex form and then be solved using standard optimization solvers.

Massive MIMO has been recognized as a key technology for 5G systems due to its high spectral efficiency. The capacity and optimal signaling for a MIMO channel under the total power constraint (TPC) are well-known and can be obtained by the water-filling (WF) procedure. However, much less is known about optimal signaling under the per-antenna power constraint constraint (PAC) or under the joint power constraints (TPC+PAC). In this thesis, we consider a massive MIMO Gaussian channel under favorable propagation (FP) and obtain the optimal transmit covariance under the joint constraints. The effect of the joint constraints on the optimal power allocation (OPA) is shown. While it has some similarities to the standard WF, it also has number of notable differences. The numbers of active streams and active PACs are obtained, and a closed-form expression for the optimal dual variable is given. A capped water-filling interpretation of the OPA is given, which is similar to the standard WF, where a container has both floor and ceiling profiles. An iterative water-filling algorithm is proposed to find the OPA under the joint constraints, and its convergence to the OPA is proven. The robustness of optimal signaling under FP is demonstrated in which it becomes nearly optimal for a nearly favorable propagation channel. An upper bound of the sub-optimality gap is given which characterizes nearly (or eps)-favorable propagation. This upper bound quantifies how close the channel is to the FP. A bisection algorithm is developed to numerically compute the optimal dual variable. Newton-barrier and Monte-Carlo algorithms are developed to find the optimal signaling under the joint constraints for an arbitrary channel, not necessarily for a favorable propagation channel. When the diagonal entries of the channel Gram matrix are fixed, it is shown that a favorable propagation channel is not necessarily the best among all possible propagation scenarios capacity-wise. We further show that the main theorems in [1] on favorable propagation are not correct in general. To make their conclusions valid, some modifications as well as additional assumptions are needed, which are given here.

Next generation wireless systems will support higher data rates, improved spectral efficiency, and less latency. Massive multiple-input multiple-output (MIMO) is proposed to satisfy these demands. In massive MIMO, many benefits come from employing hundreds of antennas at the base station (BS) and serving dozens of user terminals (UTs) per cell. As the number of antennas increases at the BS, the channel becomes sparse. By exploiting sparse channel in massive MIMO, compressive sensing (CS) methods can be implemented to estimate the channel. In CS methods, the length of pilot sequences can be shortened compared to pilot-based methods. In this paper, a novel channel estimation algorithm based on a CS method called elastic net is proposed. Channel estimation accuracy of pilot-based, lasso, and elastic-net based methods in massive MIMO are compared. It is shown that the elastic-net based method gives the best performance in terms of error for the less pilot symbols and SNR values.

In this thesis, the uplink of distributed massive MIMO where a large number of distributed access point antennas simultaneously serve a relatively smaller number of users is considered. Lattice network coding (LNC), which comprises compute and forward (C&F) and integer forcing (IF), is employed to avoid the potentially enormous backhaul load. Firstly, novel algorithms for coefficient selection in C&F are proposed. For the first time, we propose a low polynomial complexity algorithm to find the optimal solution for the complex valued case. Then we propose a sub-optimal simple linear search algorithm which is conceptually sub-optimal, however numerical results show that the performance degradation is negligible compared to the exhaustive method. The complexity of both algorithms are investigated both theoretically and numerically. The results show that our proposed algorithms achieve better performance-complexity trade-offs compared to the existing algorithms. Both algorithms are suitable for lattices over a wide range of algebraic integer domains. Secondly, the performance of LNC in a realistic distributed massive MIMO model (including fading, pathloss and correlated shadowing) is investigated in this thesis. By utilising the characteristic of pathloss, a low complexity coefficient selection algorithm for LNC is proposed. A greedy algorithm for selecting the global coefficient matrix is proposed. Comprehensive comparisons between LNC and some other promising linear strategies for massive MIMO, such as small cells (SC), maximum ratio combining (MRC), and minimum mean square error (MMSE) are also provided. Numerical results reveal that LNC not only reduces the backhaul load, but also provides uniformly good service to all users in a wide range of applications. Thirdly, the inevitable loss of information due to the quantisation and modulo operation under different backhaul constraints are investigated. An extended C\&F with flexible cardinalities is proposed to adapt to the different backhaul constraints. Numerical results show that by slightly increasing the cardinality, the gap between C\&F to the infinite backhaul case can be significantly reduced.

In the last decades the world has experienced a massive growth in the demand for wireless services. The recent popularity of hand-held devices with data exchange capabilities over wireless networks, such as smartphones and tablets, increased the wireless data traffic even further. This trend is not expected to cease in the foreseeable future. In fact, it is expected to accelerate as everyday apparatus unrelated with data communications, such as vehicles or household devices, are foreseen to be equipped with wireless communication capabilities. Further, the next generation wireless networks should be designed such that they have increased spectral and energy efficiency, provide uniformly good service to all of the accommodated users and handle many more devices simultaneously. Massive multiple-input multiple-output (Massive MIMO) systems, also termed as large-scale MIMO, very large MIMO or full-dimension MIMO, have recently been proposed as a candidate technology for next generation wireless networks. In Massive MIMO, base stations (BSs) with a large number of antenna elements serve simultaneously only a few tens of single antenna, non-cooperative users. As the number of BS antennas grow large, the normalized channel vectors to the users become pairwise asymptotically orthogonal and, therefore, simple linear processing techniques are optimal. This is substantially different from the current design of contemporary cellular systems, where BSs are equipped with a few antennas and the optimal processing is complex. Consequently, the need for redesign of the communication protocols is apparent. The deployment of Massive MIMO requires the use of many inexpensive and, potentially, off-the-shelf hardware components. Such components are likely to be of low quality and to introduce distortions to the information signal. Hence, Massive MIMO must be robust against the distortions introduced by the hardware impairments. Among the most important hardware impairments is phase noise, which is introduced by local oscillators (LOs) at the BS and the user terminals. Phase noise is a phenomenon of particular importance since it acts multiplicatively on the desired signal and rotates it by some random and unknown argument. Further, the promised gains of Massive MIMO can be reaped by coherent combination of estimated channel impulse responses at the BS antennas. Phase noise degrades the quality of the estimated channel impulse responses and impedes the coherent combination of the received waveforms. In this dissertation, wideband transmission schemes and the effect of phase noise on Massive MIMO are studied. First, the use of a low-complexity single-carrier precoding scheme for the broadcast channel is investigated when the number of BS antennas is much larger than the number of served users. A rigorous, closed-form lower bound on the achievable sum-rate is derived and a scaling law on the potential radiated energy savings is stated. Further, the performance of the proposed scheme is compared with a sum-capacity upper bound and with a bound on the performance of the contemporary multi-carrier orthogonal frequency division multiplexing (OFDM) transmission. Second, the effect of phase noise on the achievable rate performance of a wideband Massive MIMO uplink with time-reversal maximum ratio combining (TRMRC) receive processing is investigated. A rigorous lower bound on the achievable sum-rate is derived and a scaling law on the radiated energy efficiency is established. Two distinct LO configurations at the BS, i.e., the common LO (synchronous) operation and the independent LO (non-synchronous) operation, are analyzed and compared. It is concluded that the non-synchronous operation is preferable due to an averaging of the independent phase noise sources. Further, a progressive degradation of the achievable rate due to phase noise is observed. A similar study is extended to a flat fading uplink with zero-forcing (ZF) receiver at the BS. The fundamental limits of data detection in a phase-noise-impaired uplink are also studied, when the channel impulse responses are estimated via uplink training. The corresponding maximum likelihood (ML) detector is provided for the synchronous and non-synchronous operations and for a general parameterization of the phase noise statistics. The symbol error rate (SER) performance at the high signal-to-noise ratio (SNR) of the detectors is studied. Finally, rigorous lower bounds on the achievable rate of a Massive MIMO-OFDM uplink are derived and scaling laws on the radiated energy efficiency are stated.

In the first part of the dissertation, we provide an extensive overview of sub-6 GHz wireless access technology known as massive multiple-input multiple-output (MIMO) systems, highlighting its benefits, deployment challenges, and the key enabling technologies envisaged for 5G networks. We investigate the fundamental issues that degrade the performance of massive MIMO systems such as pilot contamination, precoding, user scheduling, and signal detection. In the second part, we optimize the performance of the massive MIMO system by proposing several algorithms, system designs, and hardware architectures. To mitigate the effect of pilot contamination, we propose a pilot reuse factor scheme based on the user environment and the number of active users. The results through simulations show that the proposed scheme ensures the system always operates at maximal spectral efficiency and achieves higher throughput. To address the user scheduling problem, we propose two user scheduling algorithms bases upon the measured channel gain. The simulation results show that our proposed user scheduling algorithms achieve better error performance, improve sum capacity and throughput, and guarantee fairness among the users. To address the uplink signal detection challenge in the massive MIMO systems, we propose four algorithms and their system designs. We show through simulations that the proposed algorithms are computationally efficient and can achieve near-optimal bit error rate performance. Additionally, we propose hardware architectures for all the proposed algorithms to identify the required physical components and their interrelationships.

The fifth generation of mobile communication systems (5G) promises unprecedented levels of connectivity and quality of service (QoS) to satisfy the incessant growth in the number of mobile smart devices and the huge increase in data demand. One of the primary ways 5G network technology will be accomplished is through network densification, namely increasing the number of antennas per site and deploying smaller and smaller cells. Massive MIMO, where MIMO stands for multiple-input multiple-output, is widely expected to be a key enabler of 5G. This technology leverages an aggressive spatial multiplexing, from using a large number of transmitting/receiving antennas, to multiply the capacity of a wireless channel. A massive MIMO base station (BS) is equipped with a large number of antennas, much larger than the number of active users. The users are coherently served by all the antennas, in the same time-frequency resources but separated in the spatial domain by receiving very directive signals. By supporting such a highly spatially-focused transmission (precoding), massive MIMO provides higher spectral and energy efficiency, and reduces the inter-cell interference compared to existing mobile systems. The inter-cell interference is however becoming the major bottleneck as we densify the networks. It cannot be removed as long as we rely on a network-centric implementation, since the inter-cell interference concept is inherent to the cellular paradigm. Cell-free massive MIMO refers to a massive MIMO system where the BS antennas, herein referred to as access points (APs), are geographically spread out. The APs are connected, through a fronthaul network, to a central processing unit (CPU) which is responsible for coordinating the coherent joint transmission. Such a distributed architecture provides additional macro-diversity, and the co-processing at multiple APs entirely suppresses the inter-cell interference. Each user is surrounded by serving APs and experiences no cell boundaries. This user-centric approach, combined with the system scalability that characterizes the massive MIMO design, constitutes a paradigm shift compared to the conventional centralized and distributed wireless communication systems. On the other hand, such a distributed system requires higher capacity of back/front-haul connections, and the signal co-processing increases the signaling overhead. In this thesis, we focus on some signal processing aspects of cell-free massive MIMO. More specifically, we firstly investigate if the downlink channel estimation, via downlink pilots, brings gains to cell-free massive MIMO or the statistical channel state information (CSI) knowledge at the users is enough to reliably perform data decoding, as in conventional co-located massive MIMO. Allocating downlink pilots is costly resource-wise, thus we also propose resource saving-oriented strategies for downlink pilot assignment. Secondly, we study further fully distributed and scalable precoding schemes in order to outperform cell-free massive MIMO in its canonical form, which consists in single-antenna APs implementing conjugate beamforming (also known as maximum ratio transmission).

Abstract. The problem of max-min signal-to-interference plus noise ratio (SINR) for uplink transmission of cell-free massive multiple-input multiple-output (MIMO) system is considered. We assume that the system is employed with local minimum mean square error (L-MMSE) detection. The objective is to preserve user fairness by solving max-min rate optimization problem, by optimizing transmit power of each user equipment (UE) and weighting coefficients at central processing unit (CPU), subject to a transmit power constraint at each UE. This problem is not jointly convex. Hence, we decompose original problem into two subproblems, particularly for optimizing power allocation and weight coefficients. Then, we propose an iterative algorithm to solve these two subproblems alternately. Weight coefficient subproblem is solved in the form of generalized eigen value problem while power allocation subproblem is solved by approximating as geometric programming (GP) problem.

L’évolution des communications sans fil doit répondre à la croissance exponentielle de la consommation de données. On prévoit une augmentation du débit allant jusqu’à 1000 d’ici 2020. Cependant, pour atteindre ce but, plusieurs ingrédients sont essentiels. La limitation majeure des systèmes sans fil est l’interférence à cause de la réutilisation des fréquences. C'est un problème qui existait depuis toujours et notamment à partir de la 3G. On croit que ce problème sera notamment plus grave dans la 5G, et cela à cause de la densification prévue des réseaux. L’utilisation de l’OFDM en 4G a mené à la gestion de l’interférence par coordination dynamique des blocs de ressources. Or, cela n’a permis qu’une augmentation modeste du débit. Une nouvelle technique de gestion de l’interférence fut née il y a 5 années. Cette technique s’appelle l’alignement d’interférence (IA). L’IA permet d’avoir une capacité égale à la moitié de la capacité d’un système sans interférences. Cette technique suppose que chaque transmetteur (TX) connait les canaux non seulement envers les récepteurs (RX)s mais les canaux de tous les TXs vers tous les RXs. Une technique d’interférence plus récente qui améliore l’IA, c’est le massive MIMO, ou les TXs sont équipés d’antennes à grande échelle. l’idée est motivée par plusieurs simplifications qui apparaissent en régime asymptotique ou les stations de base ont un trés grand nombre d’antennes. Le but de cette thèse est d’introduire des solutions complètes et réalistes pour la gestion d’interférence en utilisant le massive MIMO dans un scénario multi-cellules multi-utilisateurs. Notre travail traite surtout le problème de la connaissance imparfaite des canaux
The evolution of wireless communication must meet the increasingly high demand in mobile data. It is expected to increase the maximum rates of wireless by a factor of 1000 by 2020. Meanwhile, it is clear that to reach this goal, a combination of different ingredients is necessary. The major limitation of wireless systems is the interference due to frequency reuse. This has been a long-standing impairment in cellular networks of all generations that will be further exacerbated in 5G networks, due to the expected dense cell deployment. The use of orthogonal frequency-division multiplexing (OFDM) in 4G leaded to an interference management by dynamic coordination of resource blocks. However, this allowed only modest gains in rates. A new technique of interference management was born 5 years ago, the interference alignment (IA). the IA permits to have a capacity with equals the half of the capacity of an interference-free system. This technique supposes that each transmitter (TX) knows the channels not only towards its receivers (RX)s, but the channels from all TXs to all receivers RXs. A more recent interference technique that boosts IA is massive multiple input multiple output (MIMO), where TXs use antennas at a very large scale. The idea is motivated by many simplifications, which appear in an asymptotic regime where base stations are endowed with large numbers of antennas. This thesis treats the problem of interference cancellation and capacity maximization in massive MIMO. In this context, the thesis proposes new interference management alternatives for the massive MIMO antenna regime, taking into account also the practical challenges of massive antenna arrays

Nowadays, multiuser Multiple-In Multiple-Out systems (MU-MIMO) are used in a new generation wireless technologies. Due to that wireless technology improvement is ongoing, the numbers of users and applications increase rapidly. Then, wireless communications need the high data rate and link reliability at the same time. Therefore, MU-MIMO improvements have to consider 1) providing the high data rate and link reliability, 2) support all users in the same time and frequency resource, and 3) using low power consumption. In practice, the interuser interference has a strong impact when more users access to the wireless link. Complicated transmission techniques such as interference cancellation should be used to maintain a given desired quality of service. Due to these problems, MU-MIMO with very large antenna arrays (known as massive MIMO) are proposed. With a massive MU-MIMO system, we mean a hundred of antennas or more serving tens of users. The channel vectors are nearly orthogonal, and then the interuser interference is reduced significantly. Therefore, the users can be served with high data rate simultaneously. In this thesis, we focus on the performance of the massive MU-MIMO downlink where the base station uses linear precoding techniques to serve many users over Rayleigh and Nakagami-m fading channels.

The main topic of this thesis is based on multiple-input multiple-output (MIMO) wireless communications, which is a novel technology that has attracted great interest in the last twenty years. Conventional MIMO systems using up to eight antennas play a vital role in the urban cellular network, where the deployment of multiple antennas have significantly enhanced the throughput without taking extra spectrum or power resources. The massive MIMO systems “scales” up the benefits that offered by the conventional MIMO systems. Using sixty four or more antennas at the BS not only improves the spectrum efficiency significantly, but also provides additional link robustness. It is considered as a key technology in the fifth generation of mobile communication technology standards network, and the design of new algorithms for these two systems is the basis of the research in this thesis. Firstly, at the receiver side of the conventional MIMO systems, a general framework of bit error rate (BER) approximation for the detection algorithms is proposed, which aims to support an adaptive modulation scheme. The main idea is to utilize a simplified BER approximation scheme, which is based on the union bound of the maximum-likelihood detector (MLD), whereby the bit error rate (BER) performance of the detector for the varying channel qualities can be efficiently predicted. The K-best detector is utilized in the thesis because its quasi- MLD performance and the parallel computational structure. The simulation results have clearly shown the adaptive K-best algorithm, by applying the simplified approximation method, has much reduced computational complexity while still maintaining a promising BER performance. Secondly, in terms of the uplink channel estimation for the massive MIMO systems with the time-division-duplex operation, the performance of the Grassmannian line packing (GLP) based uplink pilot codebook design is investigated. It aims to eliminate the pilot contamination effect in order to increase the downlink achievable rate. In the case of a limited channel coherence interval, the uplink codebook design can be treated as a line packing problem in a Grassmannian manifold. The closed-form analytical expressions of downlink achievable rate for both the single-cell and multi-cell systems are proposed, which are intended for performance analysis and optimization. The numerical results validate the proposed analytical expressions and the rate gains by using the GLP-based uplink codebook design. Finally, the study is extended to the energy efficiency (EE) of the massive MIMO system, as the reduction carbon emissions from the information and communication technology is a long-term target for the researchers. An effective framework of maximizing the EE for the massive MIMO systems is proposed in this thesis. The optimization starts from the maximization of the minimum user rate, which is aiming to increase the quality-of-service and provide a feasible constraint for the EE maximization problem. Secondly, the EE problem is a non-concave problem and can not be solved directly, so the combination of fractional programming and the successive concave approximation based algorithm are proposed to find a good suboptimal solution. It has been shown that the proposed optimization algorithm provides a significant EE improvement compared to a baseline case.

Wireless communication technology has evolved rapidly during the last 20 years. Nowadays, there are huge networks providing communication infrastructures to not only people but also to machines, such as unmanned air and ground vehicles, cars, household appliances and so on. There is no doubt that new wireless communication technologies must be developed, that support the data traffic in these emerging, large networks. While developing these technologies, it is also important to investigate the vulnerability of these technologies to different malicious attacks. In particular, spoofing and jamming attacks should be investigated and new countermeasure techniques should be developed. In this context, spoofing refers to the situation in which a receiver identifies falsified signals, that are transmitted by the spoofers, as legitimate or trustable signals. Jamming, on the other hand, refers to the transmission of radio signals that disrupt communications by decreasing the signal-to-interference-and-noise ratio (SINR) on the receiver side.  In this thesis, we analyze the effects of spoofing and jamming both on global navigation satellite system (GNSS) and on massive multiple-input multiple-output (MIMO) communications. GNSS is everywhere and used to provide location information. Massive MIMO is one of the cornerstone technologies in 5G. We also propose countermeasure techniques to the studied spoofing and jamming attacks.  More specifically, in paper A we analyze the effects of distributed jammers on massive MIMO and answer the following questions: Is massive MIMO more robust to distributed jammers compared with previous generation’s cellular networks? Which jamming attack strategies are the best from the jammer’s perspective, and can the jamming power be spread over space to achieve more harmful attacks? In paper B, we propose a detector for GNSS receivers that is able to detect multiple spoofers without having any prior information about the attack strategy or the number of spoofers in the environment.

Massive Multiple-Input Multiple-Output (MIMO) wireless communication systems, equipped with tens or even hundreds of antennas, emerge as a promising technology for the Fifth Generation (5G) wireless communication networks. To design and evaluate the performance of massive MIMO wireless communication systems, it is essential to develop accurate, flexible, and efficient channel models which fully reflect the characteristics of massive MIMO channels. In this thesis, four massive MIMO channel models have been proposed. First, a novel non-stationary wideband multi-confocal ellipse Two-Dimensional (2-D) Geometry Based Stochastic Model (GBSM) for massive MIMO channels is proposed. Spherical wavefront is assumed in the proposed channel model, instead of the plane wavefront assumption used in conventional MIMO channel models. In addition, the Birth-Death (BD) process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Second, we propose a novel theoretical non-stationary Three-Dimensional (3-D) wideband twin-cluster channel model for massive MIMO communication systems with carrier frequencies in the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, nearfield effects instead of farfield effects are considered in the proposed model. These include the spherical wavefront assumption and a BD process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Third, a novel Kronecker Based Stochastic Model (KBSM) for massive MIMO channels is proposed. The proposed KBSM can not only capture antenna correlations but also the evolution of scatterer sets on the array axis. In addition, upper and lower bounds of KBSM channel capacities in both the high and low Signal-to-Noise Ratio (SNR) regimes are derived when the numbers of transmit and receive antennas are increasing unboundedly with a constant ratio. Finally, a novel unified framework of GBSMs for 5G wireless channels is proposed. The proposed 5G channel model framework aims at capturing key channel characteristics of certain 5G communication scenarios, such as massive MIMO systems, High Speed Train (HST) communications, Machine-to-Machine (M2M) communications, and Milli-meter Wave (mmWave) communications.

The large available spectrum efficiency and wider bandwidth at millimeter wave (mm-Wave) frequencies can enable the gigabit-per-second data rates needed for next generation wireless systems. To compensate for the high propagation loss at mm-Wave bands, multiple-input multiple-output (MIMO) with a large number of antennas are usually employed to enable beamforming. Therefore, a combination of the large number of antennas which can be called massive MIMO technology with mm-Wave bands are considered as one solution for substantially increasing the data rate for future wireless communication systems. The theoretical benefits of large antenna array systems are based on the fact that the number of radio frequency (RF) chains is equivalent to the number of antennas in the conventional wireless communication system which is known as the fully digital system. Nevertheless, implementing a large number of RF chains can be problematic since it increases the system cost, power consumption, high complexity and lowers power efficiency The overall objective of this thesis is to provide simple and effective hybrid D/A precoding and combing for mm-Wave large antenna array systems. Firstly, hybrid D/A precoding with a small number of RF chains is being considered for mm-Wave large antenna array systems. Currently, two types of antenna structures, fully-connected antenna array and partially-connected antenna array structures are adopted in the literature. Considering that each antenna array structure has its own practical advantage, in this thesis, by addressing both structures hybrid D/A precoding algorithms are proposed with target of maximizing the system's spectral efficiency with low computational complexity. For a fully-connected antenna array structure, the precoding design is formulated as an optimization problem to minimize the Euclidean distance between the hybrid D/A and the fully digital system. For a partially-connected antenna array structures, the hybrid D/A precoding is formulated as a joint D/A optimization to maximize the spectral efficiency of the system. This work further develops hybrid D/A precoding designs for mm-Wave multi-user systems based on maximizing the sum rate of the system directly. It will be shown that the proposed algorithms outperform the existing hybrid D/A precoding algorithms for the two types of structures, in terms of the spectral efficiency. Secondly, energy efficient and low complexity hybrid D/A system for mm-Wave large antenna array systems is proposed to reduce the power consumption at the system. The energy efficiency criteria is formulated as fractional programming maximization problem. The target is to find the optimal number of RF chains as the RF chains consume a high energy at the system. Therefore, the effective optimal number of RF chains of the system is found by proposing a simple search algorithm. Then, two methods are proposed for designing low complexity analog and digital precoders and combiners. The presented solutions for the hybrid D/A system are shown to be effective, as these approaches can achieve high energy efficiency, and low computational complexity as compared to the existing algorithms in hybrid D/A paradigms. Ultimately, the proposed D/A precoders and combiners based on the fully-connected antenna array attain an asymptotically optimal achievable spectral efficiency to that of the fully digital system. Thirdly, uplink multi-user hybrid D/A precoding and combining design for mm-Wave large antenna array systems is investigated. The intrinsic focus of this work is to reduce the interference of the system in the analog and digital precoders and combiners. Considering the possibility that uplink transmissions from different users can go through the paths sharing the same physical scatters, some transmission paths of different users may have overlapped angle of arrivals (AoAs) at the base station. Under this circumstance, the correlation between the channel vectors also increases highly, which affects the achievable uplink rate severely. The underlying concentrate of this work is to reduce the interference caused by users sharing the same scatterers during simultaneous uplink transmission. Therefore, in this thesis, by taking account the channel correlation between users sharing the overlapped AoAs, the genuine focus is on substantially maximizing the desired signal of a piratical user while reducing the system interference. Furthermore, the channel estimation is investigated by designing a two-step procedure. The strongest power point in each scattering point is detected and the accuracy is improved by employing an angler domain scheme. Extensive simulations demonstrate that the achievable uplink rate of our proposed algorithms surpasses the achievable uplink rate of the existing algorithms in hybrid D/A paradigms in the practical scenarios.

The non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) technologies have become integral parts of the emerging fifth-generation (5G) wireless standard. This thesis focuses on investigating the potential of integrating NOMA with massive MIMO to enable massive connectivity with high spectral efficiency and low end-to-end latency for the next generation wireless systems.

Massive MIMO (Multiple-Input Multiple-Output) base stations have proven, both in theory and in practice, to possess many of the qualities that future wireless communication systems will require.  They can provide equally high data rates throughout their coverage area and can concurrently serve multiple low-end handsets without requiring wider spectrum, denser base station deployment or significantly more power than current base stations.  The main challenge of massive MIMO is the immense hardware complexity and cost of the base station—each element in the large antenna array needs to be individually controllable and therefore requires its own radio chain.  To make massive MIMO commercially viable, the base station has to be built from inexpensive simple hardware.  In this thesis, it is investigated how the use of low-end power amplifiers and analog-to-digital converters (ADCs) affects the performance of massive MIMO.  In the study of the signal distortion from low-end amplifiers, it is shown that in-band distortion is negligible in massive MIMO and that out-of-band radiation is the limiting factor that decides what power efficiency the amplifiers can be operated at.  A precoder that produces transmit signals for the downlink with constant envelope in continuous time is presented to allow for highly power efficient low-end amplifiers.  Further, it is found that the out-of-band radiation is isotropic when the channel is frequency selective and when multiple users are served; and that it can be beamformed when the channel is frequency flat and when few users are served.  Since a massive MIMO base station radiates less power than today's base stations, isotropic out-of-band radiation means that low-end hardware with poorer linearity than required today can be used in massive MIMO.  It is also shown that using one-bit ADCs—the simplest and least power-hungry ADCs—at the base station only degrades the signal-to-interference-and-noise ratio of the system by approximately 4 dB when proper power allocation among users is done, which indicates that massive MIMO is resistant against coarse quantization and that low-end ADCs can be used.
Massiv-MIMO-basstationer (eng: Multiple-Input Multiple-Output) har visats, både i teori och praktik, besitta många av de egenskaper som framtida trådlösa kommunikationssystem kommer att behöva.  De kan tillhandahålla enhetligt höga datatakter i hela täckningsområdet och simultant betjäna flera enkla mobilenheter utan att använda bredare spektrum, tätare basstationsplacering eller betydligt mer effekt än dagens basstationer.  Huvudutmaningen med massiv MIMO är basstationens enorma hårdvarukomplexitet och -kostnad – varje element i den stora gruppantennen skall kunna kontrolleras individuellt och kräver sålunda sin egen radiokedja.  För att massiv MIMO skall bli kommersiellt attraktiv, måste basstationen byggas av billig, enkel hårdvara.  I denna avhandling undersöks hur enkla effektförstärkare och analog-till-digital-omvandlare (AD-omvandlare) påverkar massiv-MIMO-systemets prestanda.  I studien av signaldistorsionen från enkla förstärkare visas det att inband-distorsionen är försumbar i massiv MIMO och att utombandsstrålningen är den begränsande faktorn som bestämmer vid vilken verkningsgrad förstärkarna kan arbeta.  En förkodare som åstadkommer nerlänks-sändsignaler som har konstant envelopp i kontinuerlig tid presenteras för att möjliggöra användandet av enkla förstärkare med hög verkningsgrad.  Vidare konstateras det att utombandsstrålningen är isotrop när kanalen är frekvensselektiv och när flera användare betjänas; och att den kan lobformas när kanalen är frekvensflat och när få användare betjänas.  Eftersom en massiv-MIMO-basstation utstrålar mindre effekt än dagens basstationer, betyder isotrop utombandsstrålning att enkel hårdvara med sämre linearitet än vad som krävs idag kan användas i massiv MIMO.  Det visas även att användandet av enbits-AD-omvandlare – de enklaste och mest strömsnåla AD-omvandlarna – i basstationen endast minskar signal-till-interferens-och-brus-förhållandet med 4 dB när tillbörlig effektallokering mellan användarna utförs, vilket indikerar att massiv MIMO är motståndskraftig mot grov kvantisering och att enkla AD-omvandlare kan användas.
大規模多輸入多輸出基站，無論從理論上或實際上，皆已經證明具有許多未來無線通訊系統所需的特質。比如：在其整個覆蓋區域均一地提供高數據傳輸速率、在同一時間頻率資源上服務多個簡單的終端設備，而無需佔用更多頻譜資源或更密集地部署基站，亦無需提高基站的功耗。實現大規模多入多出系统的主要挑戰在於硬件複雜度及基站成本——大規模天線陣列中的每一個天線元必須單獨可控，因此需要其自身的射頻鏈路。爲使大規模多入多出基站有商業吸引力，基站必須以簡單低成本的硬件來建造。本論文探討簡單的功率放大器與模擬數字轉換器對大規模多入多出性能的影響。對低端功放信號失真的研究表明，帶內失真對大規模多入多出的性能影響幾乎可以忽略，而帶外泄露是限制功放效率的決定因素。爲使用高功率效率低端功放，本文提出能產生具有恆定包絡連續時間信號的預編碼。本文指出，在頻率選擇性衰落信道上服務多個用戶時，帶外泄露呈現各向同性；而在平坦衰落信道上服務少數用戶時，帶外泄露可呈現波束賦形。由於大規模多入多出基站比現用基站輻射較少功率，帶外泄露各向同性意味著大規模多入多出基站可使用低端硬件，其線性要求不比現有基站的高。另外表明，如果進行合理的多用戶功率分配，基站使用單比特模擬數字轉換器——最簡單低耗的轉換器——僅使系統的信干噪比降低約４分貝。以此可見，大規模多入多出系統對非精確量比較穩定，低端模擬數字轉換器可於此類系統中使用。

La demande croissante en efficacité spectrale, stimulée par les exigences strictes des réseaux 5G, a accéléré le développement de la technologie MIMO en ondes millimétriques, offrant des améliorations architecturales significatives grâce à des techniques de précodage avancées. Cette technologie présente des gains substantiels en termes d'efficacité spectrale et énergétique par rapport aux systèmes MIMO traditionnels. Cependant, le potentiel transformateur du MIMO en mmWave est entravé par les réalités complexes des environnements urbains réels et les propriétés physiques complexes inhérentes aux fréquences des ondes millimétriques.De manière cruciale, dans les communications massives MIMO en mmWave, le beamforming et le combining jouent des rôles essentiels : la large bande passante et la fréquence de fonctionnement élevée des systèmes à ondes millimétriques nécessitent un beamforming/combining dans le domaine analogique, rendant les approches entièrement digitales techniquement impossibles. Au cœur du MIMO massif en mmWave se trouve le problème d'Alignement des Faisceaux, exigeant l'identification des paires de faisceaux d'émission et de réception optimales qui maximisent le rapport signal/bruit, assurant ainsi une liaison initiale robuste.Les normes existantes, telles que WiGig, utilisent des méthodes exhaustives de sondage des faisceaux, testant chaque paire de faisceaux possible pour trouver celle qui maximise le SNR. Cependant, cela entraîne un surcoût important de signalisation de pilotes: le principal problème que nous cherchons à résoudre tout au long de cette thèse de doctorat. Notre recherche révolutionne l'Alignement des Faisceaux en intégrant des techniques de pointe en apprentissage automatique pour l'Alignement Partiel des Faisceaux, réduisant considérablement les surcharge de pilotes en ne sondant qu'un sous-ensemble de paires de faisceaux à l'aide de codebooks sous-échantillonnés. Ainsi, nous exploitons les énergies des signaux reçus à partir de ces sondages de paires de faisceaux, en utilisant des réseaux neuronaux peu profonds, la factorisation matricielle et leurs variantes pour résoudre avec précision des problèmes de régression non-linéaire et logistique, cruciaux pour déterminer la qualité des paires de faisceaux restantes.Un objectif fondamental de cette thèse est de déterminer la complexité de l'échantillonnage pour ces méthodes d'apprentissage automatique. Cette complexité dicte le nombre minimum d'échantillons d'entraînement nécessaires pour un apprentissage efficace et une transmission fiable. Nous examinons également les performances des modèles ML proposés sans estimation préalable du canal, introduisant le concept d'Alignement Aveugle des Faisceaux, ouvrant ainsi la voie à un changement radical de paradigme. De plus, notre recherche explore en profondeur les subtilités de la quantification, une contrainte pratique vitale. Nous explorons ensuite des compromis cruciaux : identifier la surcharge minimale correspondant au schéma de quantification optimal tout en investiguant le compromis classique entre précision et complexité.Grâce à une progression méthodologique systématique, allant des scénarios point-à-point basiques à bande étroite aux complexes architectures multi-utilisateurs à large bande, cette thèse de doctorat offre des insights et des solutions précieuses: les contributions proposées font progresser les domaines des communications en mmWave et les applications d'apprentissage automatique dans les systèmes sans fil, surpassant les benchmarks existants et affrontant les limites des approches conventionnelles
The escalating demand for spectral efficiency driven by the stringent requirements of 5G networks has spurred the development of mmWave MIMO technology, promising significant architectural improvements through advanced precoding techniques. This technology presents substantial gains in spectral and energy efficiencies compared to traditional MIMO systems. However, the transformative potential of mmWave MIMO is hampered by the complex realities of real-world urban environments and the intricate physical properties inherent to mmWave frequencies.Crucially, in mmWave massive MIMO communication, beamforming and combining play pivotal roles: the high bandwidth and operating frequency of mmWave systems necessitate analog domain beamforming/combining, rendering fully digital approaches technically non feasible. At the heart of mmWave large-dimensional MIMO lies the Beam Alignment problem, requiring the identification of optimal transmit and receiver beam pairs that maximize the Signal-to-Noise ratio, ensuring a robust initial link.Existing standards, such as WiGig, employ exhaustive beam sounding methods, testing each possible beam pair to find the one maximizing SNR. Consequently, it leads to substantial pilot-signaling overhead, the major problem we aim to encounter throughout this PhD. Our research revolutionizes Beam Alignment by integrating cutting-edge machine learning techniques for Partial Beam Alignment, significantly reducing the pilot overhead by soundings a subset of beam pairs using sub-sampled codebooks. Therefore, we leverage the received signal energies from these beam pairs soundings, employing shallow neural networks, matrix factorization, and their variants for accurately resolving non-linear and logistic regression problems, crucial for determining the quality of the remaining beam pairs.A fundamental objective of this thesis is to determine the sample complexity for these machine learning methods. This complexity dictates the minimum number of training samples necessary for effective learning and reliable transmission. We delve into the performance of the proposed ML models without prior channel estimation, introducing the concept of Blind Beam Alignment, thus pioneering a paradigm shift. Furthermore, our research delves deep into the nuances of quantization, a vital practical constraint. We then explore critical compromises: identifying the minimum overhead ratio corresponding to the optimal quantization scheme on the one hand and navigating the classic trade-off between accuracy and complexity on the other hand.Through systematic progression, ranging from basic point-to-point narrowband scenarios to intricate wideband multi-user architectures, this PhD thesis offers valuable insights and solutions. The proposed contributions advance the fields of mmWave communications and Machine Learning applications in wireless systems, outperforming existing benchmarks, and encountering the limitations of conventional approaches

Cette thèse présente des mesures in-situ d'une antenne massive MIMO et analyse les différents paramètres pertinents pour l'estimation de l'exposition aux CEM dans un réseau 5G. De multiples méthodes sont présentées et discutées pour estimer la puissance reçue dans le réseau tout en se concentrant sur les avantages et les inconvénients de chacune d'elles.Cette thèse propose également une nouvelle méthode analytique pour étudier l'exposition moyenne, représentée par la puissance totale reçue, dans un réseau 5G massive MIMO dans la bande mmWave. En utilisant la géométrie stochastique, une équation de forme proche de l'exposition est développée et étudiée en adaptant un modèle de canal mmWave utilisant NYUSIM dans des distributions statistiques et en modélisant les BS en tant que PPP. Une analyse de sensibilité est effectuée pour quantifier l'influence des variables d'entrée sur l'exposition.Un autre modèle de réseau massive MIMO multi-utilisateurs déployant le précodage MRC et le contrôle de puissance max-min fairness est également développé, et où les MTs sont distribués suivant un PPP soit en LoS ou NLoS. Une expression sous forme fermée de l'espérance de la puissance totale reçue et l'expression du rapport entre la puissance totale et le SIR à la MT la plus proche de sa BS de desserte, où l'exposition est la plus élevée. L'exposition moyenne est ensuite étudiée par rapport aux paramètres du réseau en tenant compte des compromis présentés par le modèle de contrôle de puissance et les gains d'antenne. De même, le rapport entre l'exposition et le SIR est également analysé pour étudier l'augmentation de l'exposition par l'augmentation du SIR à la MT la plus proche de sa BS. Et il est montré que plus le nombre d'éléments d'une antenne massive MIMO est élevé, plus elle est efficace en termes de SIR considérant l'exposition produite
This dissertation presents in-situ measurements of a massive MIMO antenna and analyses the different parameters pertinent to the estimation of the EMF exposure in a 5G network. Multiple methods are presented and discussed to estimate the power received in the network while focusing on the advantages and inconveniences in each of them. This dissertation also proposes a new analytical method for studying the average exposure, presented by the total power received, in a 5G mmWave massive MIMO network. Using stochastic geometry, a close-form equation of the exposure is developed and studied by fitting a mmWave channel model using NYUSIM into statistical distributions and by modeling the BSs as a PPP. A sensitivity analysis is performed to quantify the influence of the input variables onto the exposure.Another model for a multi-user massive MIMO network is also developed deploying maximum-ratio combining precoding and max-min fairness downlink power control, and where MTs are distributed following a PPP and can be either LoS or NLoS. A closed-form expression of the expectation of the total power received and the expression of the ratio between the total power and the SIR at the nearest MT to its serving BS, where the exposure is highest. The average exposure is then studied in relation to network parameters taking into account the trade-offs presented by the power control model and antenna gains. Likewise, the ratio between the exposure and SIR is also analyzed to study the increase of exposure per the increase of the SIR at the nearest MT to its BS. And it is shown that the higher the number of antenna elements a massive MIMO antenna has, the more efficient it is in terms of SIR considering the produced exposure

Les systèmes sans fil de la prochaine génération doivent répondre à la demande croissante de débits de données de plus en plus élevés à des prix très compétitifs et être capables de s'adapter efficacement à une vaste gamme dynamique de services, d'applications et de types de dispositifs attendus dans un avenir proche. Les systèmes sans fil ultra-denses envisagent des UD-DAS basés sur des antennes distribuées à distance. Toutefois, ni les systèmes d'antennes distribuées ni la technologie MIMO massive ne permettront de répondre aux demandes de débit de données croissantes de la prochaine génération de communications sans fil, en raison des interférences intercellulaires et des grandes variations de qualité de service. Pour faire face à ces limitations, les réseaux au-delà de la 5G doivent entrer dans le paradigme sans cellule, où l'absence de limites de cellules atténue les interférences intercellulaires et les problèmes de transfert, mais pose également de nouveaux défis. La première partie de cette thèse est consacrée à l'analyse des propriétés de propagation favorables des systèmes MIMO massifs CF dans des conditions asymptotiques. L'information sur l'état du canal dans les systèmes MIMO massifs, tant cellulaires que CF, joue un rôle majeur dans l'amélioration des performances du système. Par conséquent, dans la deuxième partie de cette thèse, nous abordons le problème de la contamination des pilotes dans les systèmes MIMO massifs CF. Enfin, dans la dernière partie de cette thèse, nous proposons un algorithme MP basé sur le principe EP pour conduire itérativement la méthode semi-aveugle bayésienne pour l'estimation du canal et la détection des données
Next generation wireless systems shall satisfy the increasing demand of higher and higher data rates at very competitive prices as well as be able to efficiently accommodate for and adapt to a huge dynamic range of services, applications, and types of devices expected in the near. Appealing architectural solutions have been leveraged on ultra-densification of antennas. Ultra-dense wireless systems envision ultra-dense distributed antenna systems (UD-DAS) based on remote distributed antennas empowered by the e-cloud. However, neither DAS nor massive MIMO technology will meet the increasing data rate demands of the next generation wireless communications due to the inter-cell interference and large quality of service (QoS) variations. To address these limitations, beyond-5G networks need to enter the cell-free (CF) paradigm, where the absence of cell boundaries mitigates the inter-cell interference and handover issues but also causes new challenges. One of the major issues in the large-scale networks such as CF massive MIMO systems is complexity at the receivers. In this regard, first part of this thesis is devoted to analyzing the favorable propagation properties of CF massive MIMO systems in asymptotic conditions. Channel state information (CSI) in massive MIMO systems, both cellular and CF, plays a major role in improving the system performance. Therefore, in the second part of this thesis, we address the pilot contamination problem in CF massive MIMO systems. Finally, in the last part of this thesis, we propose an MP algorithm based on the EP principle to iteratively conduct the Bayesian semi-blind method for channel estimation and data detection in CF massive systems

The past decade has seen an enormous increase in the number of connected wireless devices, and currently there are billions of devices that are connected and managed by wireless networks. At the same time, the applications that are running on these devices have also developed significantly and became more data rate insatiable. As the number of wireless devices and the demand for a high data rate will always increase, in addition to the growing concern about the energy consumption of wireless communication systems, the future wireless communication systems will have to meet three main requirements. These three requirements are: i) being able to achieve high throughput; ii) serving a large number of users simultaneously; and iii) being energy efficient (less energy consumption). Massive multiple-input multiple-output (MIMO) technology can satisfy the aforementioned requirements; and thus, it is a promising candidate technology for the next generations of wireless communication systems. Massive MIMO technology simply refers to the idea of utilizing a large number of antennas at the base station (BS) to serve a large number of users simultaneously using the same time-frequency resources. The hypothesis behind using a massive number of antennas at the BS is that as the number of antennas increases, the channels become favourable. In other words, the channel vectors between the users and their serving BS become (nearly) pairwisely orthogonal as the number of BS antennas increases. This in turn enables the use of linear processing at the BS to achieve near optimal performance. Moreover, a huge throughput and energy efficiency can be attained due to users multiplexing and array gain. In this thesis, we investigate the performance of massive MIMO systems under different scenarios. Firstly, we investigate the performance of a single-cell multi-user massive MIMO system, in which the channel vectors for the different users are assumed to be correlated. In this aspect, we propose two algorithms for users grouping that aim to improve the system performance. Afterwards, the problem of pilot contamination in multi-cell massive MIMO systems is discussed. Based on this discussion, we propose a pilot allocation algorithm that maximizes the minimum achievable rate in a target cell. Following that, we consider two different scenarios for pilot sequences allocation in multi-cell massive MIMO systems. Lower bounds on the achievable rates are derived for two linear detectors, and the performance under different system settings is analysed and discussed for both scenarios. Finally, two algorithms for pilot sequences allocation are proposed. The first algorithm takes advantage of the multiplicity of pilot sequences over the number of users to improve the achievable rate of edge cell users. While the second algorithm aims to mitigate the negative impact of pilot contamination by utilizing more system resources for the channel estimation process to reduce the inter-cell interference.

As we progress towards the fifth generation (5G) of wireless networks, the bit-per-joule energy efficiency (EE) metric becomes an important design criterion because it allows for operation at practically affordable energy consumption levels. In this regard, one of the key technology enablers for 5G is the recently proposed massive multiple-input multiple-output (MIMO) technology, which is a special case of multiuser MIMO with an excess of base station (BS) antennas. However, techniques for extracting large EE gains from massive MIMO (MM) networks have not been actively investigated so far. We seek to address the above limitation in this thesis by (i) reviewing MM technology from an EE perspective, (ii) critically analyzing the state-of-the-art and proposing new research directions for EE-maximization in “hybrid MM” networks, where massive MIMO operates alongside other emerging 5G technologies, and (iii) proposing a novel resource allocation scheme for EE-maximization in MM networks. The thesis consists of three main parts. In the first part, we motivate the need for EE and explain why massive MIMO is promising as an energy-efficient technology enabler for 5G networks. In the second part, we critically analyze opportunities for EE-maximization in three types of hybrid MM networks, namely, millimeter wave based MM networks, MM-based heterogeneous networks, and energy har- vesting based MM networks. We analyze limitations in the state-of-the-art and propose several promising research directions which, if pursued, will immensely help network opera- tors in designing hybrid MM networks. In the third part, we propose a novel EE-maximization scheme which optimizes resource allocation in an MM network. Three communication resources, namely, the number of BS antennas, pilot power, and data power are optimized for EE. Since the optimization problem is difficult to solve in its original form, we propose a novel solution approach where each iteration solves a sequence of difference of convex programming subproblems. Simulation results render few interesting guidelines for network designers. For example, using higher pilot power than data power can improve the system EE, particularly when SNR is high. Also, the number of BS antennas should be optimized with the available power budget to ensure operation at peak EE.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

Multi-user (MU) massive multiple-input-multiple-output (MIMO) is one of the promising technologies for the 5th Generation of wireless communication systems. However, as an emerging technology, various technical challenges that hinder practical use of massive MIMO need to be addressed, e.g., imperfections on channel estimation and channel reciprocity. The overall objective of the proposed research is to investigate some of the key practical challenges of implementation of the massive MIMO system and propose effective solutions for those problems. First, in order to realise promised benefits of massive MIMO, there is a need for a highly accurate technique for provisioning of channel state information (CSI). However, the acquisition of CSI can be considerably influenced by imperfect channel estimation in practice. We therefore analyse the impact of channel estimation error on the performance of massive MIMO uplinks with the considerations of the channel correlation over space. We then propose a novel antenna selection scheme by exploiting the sparsity of the channel gain matrix at the received end, which significantly reduces implementation overhead and complexity compared to the well-adopted scheme, without degrading the system performance. Second, it is known that channel reciprocity in time-division duplexing (TDD) massive MIMO systems can be exploited to reduce the overhead required for the acquisition of CSI. However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. We model and analyse the impact of the RF mismatches by taking into account the channel estimation error. We derive closed-form expressions of the output signal-to-interference-plus- noise ratio for typical linear precoding schemes, and further investigate the asymptotic performance of the considered precoding schemes to provide insights into the practical system designs, including guidelines for the selection of the effective precoding schemes. Third, our theoretical model for analysing the effect of channel reciprocity error on massive MIMO systems reveals that the imperfections in channel reciprocity might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two calibration schemes for TDD-based MU massive MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. To compare two calibration schemes, we derive closed-form expressions for the ergodic sum-rate and the receive mean-square error for downlinks. We demonstrate that the proposed inverse calibration outperforms the relative calibration, thanks to its greater robustness to the compound effect of both errors.

The aim of this thesis is to analyze the uplink massive multiple-input multipleoutput with orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems and to design a receiver that has improved performance with reduced complexity. First, a novel receiver is proposed for coded massive MIMO-OFDM systems utilizing log-likelihood ratios (LLRs) derived from complex ratio distributions to model the approximate effective noise (AEN) probability density function (PDF) at the output of a zero-forcing equalizer (ZFE). These LLRs are subsequently used to improve the performance of the decoding of low-density parity-check (LDPC) codes and turbo codes. The Neumann large matrix approximation is employed to simplify the matrix inversion in deriving the PDF. To verify the PDF of the AEN, Monte-Carlo simulations are used to demonstrate the close-match fitting between the derived PDF and the experimentally obtained histogram of the noise in addition to the statistical tests and the independence verification. In addition, complexity analysis of the LLR obtained using the newly derived noise PDF is considered. The derived LLR can be time consuming when the number of receive antennas is very large in massive MIMO-OFDM systems. Thus, a reduced complexity approximation is introduced to this LLR using Newton’s interpolation with different orders and the results are compared to exact simulations. Further simulation results over time-flat frequency selective multipath fading channels demonstrated improved performance over equivalent systems using the Gaussian approximation for the PDF of the noise. By utilizing the PDF of the AEN, the PDF of the signal-to-noise ratio (SNR) is obtained. Then, the outage probability, the closed-form capacity and three approximate expressions for the channel capacity are derived based on that PDF. The system performance is further investigated by exploiting the PDF of the AEN to derive the bit error rate (BER) for the massive MIMO-OFDM system with different M-ary modulations. Then, the pairwise error probability (PEP) is derived to obtain the upper-bounds for the convolutionally coded and turbo coded massive MIMO-OFDM systems for different code generators and receive antennas. Furthermore, the effect of the fixed point data representation on the performance of the massive MIMO-OFDM systems is investigated using reduced detection implementations for MIMO detectors. The motivation for the fixed point analysis is the need for a reduced complexity detector to be implemented as an optimum massive MIMO detector with low precision. Different decomposition schemes are used to build the linear detector based on the IEEE 754 standard in addition to a user-defined precision for selected detectors. Simulations are used to demonstrate the behaviour of several matrix inversion schemes under reduced bit resolution. The numerical results demonstrate improved performance when using QR-factorization and pivoted LDLT decomposition schemes at reduced precision.

The complexity of advanced antenna techniques used in the new generation of wireless networks (5G) makes communication between experts and non-technical staff more difficult than ever. As cooperation between network vendors and network operators affects the adoption of the new standard, a need for a new tool has emerged to make technical presentations more engaging and compelling. This thesis presents an exploratory study that aims to examine various design options for an interactive visualization of radiowave propagation to be used by advanced antenna systems experts. Through a Research-oriented Design, functional and non-functional requirements were identified with the help of domain expert. Later, an interactive prototype was designed and developed using a participatory design approach. Qualitative and quantitative data was gathered through usability testing, System Usability Scale (SUS) questionnaires and semi-structured interviews conducted with 12 researchers and engineers at Ericsson AB a multinational telecommunication company. User evaluation proved that such a tool could facilitate communication between technical experts and non-technical staff. The developed prototype was considered intuitive and useful by the majority of study participants as measured by interviews and the SUS survey. Future research is encouraged to include the target audience representatives in order to measure their engagement while using the tool.
Komplexiteten hos avancerade antenntekniker som anvnds i den nya generationen av mobilntverk (5G), gr kommunikationen mellan experter och icke-teknisk personal svrare n ngonsin. Eftersom samarbetet mellan telekommunikationsfretag och ntoperatrer pverkar anpassningen av den nya standarden, har behovet av ett nytt verktyg uppsttt fr att gra tekniska presentationer mer engagerande och vertygande. Avhandlingen presenterar en underskande studie som syftar till att underska olika designalternativ fr en interaktiv visualisering av radiovgsfrkning som anvnds av avancerade antennsystems experter. Genom en forskningsinriktad design identifierades funktionella och icke-funktionella krav med hjlp av en domnexpert. Senare konstruerades och utvecklades en interaktiv prototyp med hjlp av en co-operativ designmetod. Kvalitativa och kvantitativa data samlades in genom anvndbarhetstester, System Usability Scale (SUS) frgeformulr och halvstrukturerade intervjuer med 12 forskare och ingenjrer p Ericsson AB ett multinationellt telekommunikationsfretag. Anvndarutvrdering visade att ett sdant verktyg skulle underltta kommunikationen mellan tekniska experter och icke-teknisk personal. Den utvecklade prototypen ansgs intuitiv och anvndbar av majoriteten av studiedeltagarna, mtt genom intervjuer och SUS-underskningen. Framtida forskning uppmuntrar till att inkludera mlgruppsrepresentanterna fr att mta deras engagemang medan de anvnder verktyget.

Over the past few years there has been an extensive growth in data traffic consumption devices. Billions of mobile data devices are connected to the global wireless network. Customers demand revived services and up-to-date developed applications, like real-time video and games. These applications require reliable and high data rate wireless communication with high throughput network. One way to meet these requirements is by increasing the number of transmit and/or receive antennas of the wireless communication systems. Massive multiple-input multiple-output (MIMO) has emerged as a promising candidate technology for the next generation (5G) wireless communication. Massive MIMO increases the spatial multiplexing gain and the data rate by adding an excessive number of antennas to the base station (BS) terminals of wireless communication systems. However, building efficient algorithms able to decode a coherently or non-coherently large flow of transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we propose novel approaches to achieve optimal performance for joint channel estimation and signal detection for massive MIMO systems. The dissertation consists of three parts depending on the number of users at the receiver side. In the first part, we introduce a probabilistic approach to solve the problem of coherent signal detection using the optimized Markov Chain Monte Carlo (MCMC) technique. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: The probability of encountering the optimal solution when the Markov chain converges to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal value of the “temperature'' parameter such that the MC encounters the optimal solution in a polynomially small probability. Second, we study the mixing time of the underlying Markov chain of the proposed MCMC detector. We assume the channel state information is known in the first part of the dissertation; in the second part we consider non-coherent signal detection. We develop and design an optimal joint channel estimation and signal detection algorithms for massive (single-input multiple-output) SIMO wireless systems. We propose exact non-coherent data detection algorithms in the sense of generalized likelihood ratio test (GLRT). In addition to their optimality, these proposed tree based algorithms perform low expected complexity and for general modulus constellations. More specifically, despite the large number of the unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas (N) and polynomial in channel coherence time (T). We prove that as $N \rightarrow \infty$, the number of tested hypotheses for each coherent block equals $T$ times the cardinality of the modulus constellation. Simulation results show that the optimal non-coherent data detection algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity. In the part three, we consider massive MIMO uplink wireless systems with time-division duplex (TDD) operation. We propose an optimal algorithm in terms of GLRT to solve the problem of joint channel estimation and data detection for massive MIMO systems. We show that the expected complexity of our algorithm grows polynomially in the channel coherence time (T). The proposed algorithm is novel in two terms: First, the transmitted signal can be chosen from any modulus constellation, constant and non-constant. Second, the algorithm decodes the received noisy signal, which is transmitted a from multiple-antenna array, offering exact solution with polynomial complexity in the coherent block interval. Simulation results demonstrate significant performance gains of our approach compared with suboptimal non-coherent detection schemes. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.

Com a chegada da quinta geração de comunicações móveis (5G) espera-se que os sistemas de comunicação sem fios possam oferecer novos e melhores serviços com ritmos de transmissão elevadíssimos. O aumento significativo do bit rate que se prevê com o incremento dos utilizadores e dos dispositivos a ligar a rede, a entrada da IoT e outas tecnologias, isto sem perder de vista as inúmeras dificuldades da elevada dispersão associada à propagação multipercurso dos sinais, as elevadas taxas de eficiência e potência exigidas e a grande capacidade e flexibilidade esperadas nos novos sistemas, são fatores que contribuem para um aumento significativo da complexidade do sistema. A solução de todas estas prolemáticas torna possível o desenvolvimento destas novas tecnologias. A tecnologia massive MIMO apresenta-se como um forte candidato com potencial para satisfazer todas as necessidades exigidas pela nova quinta geração de comunicações móveis e para lidar com todas as futuras tecnologias de forma eficiente, segura, fiável. Contudo, todos estes benefícios trazem consigo um enorme aumento da complexidade devido aos múltiplos sinais envolvidos na transmissão de dezenas de antenas. Por outro lado, existem técnicas que implementam ambientes de enorme complexidade utilizando esquemas de recepção OFDM, SC-FDE e IB-DFE e que oferecem ótimos resultados em termos de eficiência mas que resultam numa enorme complexidade devido a operações matriciais envolvidas na sua lógica. Existem também algoritmos de processamento como MRC e EGC que por não incluir operações demasiado complexas na sua lógica contribuem para uma diminuição significativa da complexidade embora, a custa de uma perda de eficiência considerável. Este trabalho científico traz como proposta a implementação dum receptor de má- ximo desempenho do tipo IB-DFE combinado com técnicas MRC/EGC, capaz de operar em ambientes Massive MIMO, no sentido uplink da ligação, e no domínio da frequência. Desta forma será possível tirar o máximo partido de ambas as abordagens de maneira a garantir o máximo desempenho do sistema e uma redução da complexidade de implementação.
As the 5th Generation of wireless comunications approaches we antecipate the provision of better services with much higher transmission speeds. This leads inevitably to an increase of the devices and users of the network, due to new technologies, such as the IoT. On the other hand there are numerous difficulties associated with high signal dispersion due to its multipath propagation, highth rates and high power efficiency as well as the expection of larger capacity and flexibility in this new system. In a nutshell: the growth of the complexity of these systems is the great challenge of the 5G. The answer for all this issues is essencial for the development of these wireless technologies. The massive MIMO technology presents itself as a strong candidate for the requirements demanded by 5G and it promises to be efficient, safe and reliable. However, all these benefits bring a huge increase of complexity due to the multiple signals involved in the transmission of a large number of antennas. On the other hand, there are techniques that implement very complex environments employing OFDM, SC-FDE and IB-DFE reception schemes, which offer great performance in terms of efficiency at the expense of an increase of complexity due to matrix opperations involved. Besides, there are algorithms that employ MRC and EGC techniques that help to significantly reduce the complexity of the system since they do not include matrix operations. Notwithstanding, these techniques lose performance to the other ones. Having said that, our proposal is the implementation of an optimal performance frequency-domain IB-DFE receiver, combined with MRC/EGC techniques, that is able to perform in massive MIMO environment and uplink transmission. Therefore we will be able to get the best of both approaches, ensuring the optimal performance of the system and a reduction of the complexity of the implementation.

Next generation of mobile communication systems must support astounding data traffic increases, higher data rates and lower latency, among other requirements. These requirements should be met while assuring energy efficiency for mobile devices and base stations. Several technologies are being proposed for 5G, but a consensus begins to emerge. Most likely, the future core 5G technologies will include massive MIMO (Multiple Input Multiple Output) and beamforming schemes operating in the millimeter wave spectrum. As soon as the millimeter wave propagation difficulties are overcome, the full potential of massive MIMO structures can be tapped. The present work proposes a new transmission system with bi-dimensional antenna arrays working at millimeter wave frequencies, where the multiple antenna configurations can be used to obtain very high gain and directive transmission in point to point communications. A combination of beamforming with a constellation shaping scheme is proposed, that enables good user isolation and protection against eavesdropping, while simultaneously assuring power efficient amplification of multi-level constellations.

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance.

We face now an exponential increase in wireless devices and to allow good user experience, it is imperative that the next generation of mobile (5G) communications ensures reliable connections, high data transfer rates and low latency. One way to increase the data transfer rate is to use massive Multiple-Input, Multiple-Output (MIMO) systems, that is, systems with multiple antennas to emit and multiple antennas to receive thus allowing spatial diversity. In these systems, to increase the battery life of the devices it is preferable to use the Single-Carrier with Frequency-Domain Equalization modulation in the uplink as this modulation reduces the complexity in the emitter, transferring it to the receiver, in this case the Base Staion, where it is quite acceptable. This dissertation studies the performance of massive MIMO receiver systems, comparing it to the performance achieved with the Matched Filter Bound (MFB). The Iterative Block Decision-Feedback Equalizer (IB-DFE) receiver presents a very similar performance to the MFB, however, the algorithm requires matrix inversions, which in the systems under study, where the size of the matrix is high, implies an increase of the associated operations increases. Thus it is very important that low complexity receivers, such as the Maximal-Ratio Combining (MRC) or Equal Gain Combining are used. In this dissertation, a simple receiver is proposed combining the IB-DFE receiver with the MRC receiver, thus creating a low complexity receiver with excellent performance
Atualmente, sente-se um aumento exponencial nos dispositivos wireless. De modo a permitir uma boa experiência por parte dos utilizadores é fundamental que a próxima geração de comunicações móveis (5G) assegure fiabilidade nas ligações, uma elevada taxa de transferência de dados e baixa latência. Uma maneira de elevar a taxa de transferência de dados é utilizar sistemas massive Multiple-Input, Multiple-Output (MIMO), ou seja, sistemas com múltiplas antenas a emitir e múltiplas antenas a receber permitindo assim diversidade espacial. Nestes sistemas, para aumentar a bateria dos dispositivos é preferível usar no uplink a modulação Single-Carrier with Frequency-Domain Equalization pois esta modulação reduz a complexidade no emissor transferindo-a para o recetor, neste caso na Base Station, onde isso é bastante aceitável. Esta dissertação estuda o desempenho dos recetores dos sistemas massive MIMO, comparando o desempenho alcançado com o desempenho do Matched Filter Bound (MFB). O recetor Iterative Block Decision-Feedback Equalizer (IBDFE) apresenta um desempenho muito semelhante ao do MFB no entanto, o algoritmo do receptor inverte matrizes, o que nos sistemas em estudo, onde o tamanho das matrizes é elevado, se reflecte no aumento da complexidade das operações associadas. Deste modo, é importante que sejam utilizados recetores de baixa complexidade tal como o Maximal-Ratio Combining (MRC) ou o Equal Gain Combining. Esta dissertação propõe um recetor simples que combina um recetor IB-DFE com um recetor MRC, criando desde modo um recetor de baixa complexidade e com excelente desempenho.

碩士
國立中央大學
通訊工程學系
105
Massive MIMO systems are popular studies in recent years. Differential encoding scheme is not required to transmit pilot sequence for channel estimation, so it does not cause pilot contamination. A differentially encoded QAM (quadrature amplitude modulation) scheme for uplink massive MIMO was proposed recently which outperforms conventional differential APSK (amplitude-phase shift keying) scheme for 1000 receiver antennas. In this letter, we raise and try to resolve some questions about this differential QAM scheme. We construct new tables for differentially encoded 16-QAM. We compare differential 16-QAM with differential 16-APSK for the same detector, and compare the detector with a conventional detector for the same transmitter. Simulation results show that differential 16-APSK has the best error performance for any number of receiver antennas. Besides, we propose a new scheme for uplink coherent massive MIMO. This scheme uses transmitted data signal as pilot sequences, and we also design the pilot sequences to make it orthogonal to each other, which can shorten the time of transmission of the pilot sequence.

碩士
國立暨南國際大學
電機工程學系
107
Along with the development of the 5G communication systems, the requirement for data transmission and lower costs keeps increasing. This thesis presents the hybrid beamforming scheme using mmWave for large-scale multi input multiple output (Massive MIMO)antenna system. Because hybrid beamforming (HBF) can be utilized to greatly reduce the number of RF chains, while can effectively increasing capacity to achieve the demand of the 5G system. In this thesis, we proposed three kinds of hybrid beamforming (HBF) schemes using mmWave for Massive MIMO antenna system. In these schemes, the analog beamforming based on antenna selection and conventional beamforming is used to saving active antenna, to reduce the costs, and the digital beamforming based on minimum mean-squared error (MMSE), to improve the capacity. As shown by the Simulation results, these proposed schemes will be discussed in terms of three aspects and will demonstrated the benefits of proposed schemes separately.