Готові списки джерел за темами / Massives MIMO

Добірка наукової літератури з теми "Massives MIMO"

Автор: Grafiati
Опубліковано: 23 вересня 2022
Оновлено: 27 січня 2023

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Статті в журналах з теми "Massives MIMO":

1

Sharma, Manmohan, Sunny Verma, and Shekhar Verma. "Optimization of Cell-Free Massive MIMO System." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012056. http://dx.doi.org/10.1088/1742-6596/2327/1/012056.

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Abstract As an innovative implementation, Cell-Free Massive Multiple Input Multiple Output (MIMO) has appeared in typical Cellular Massive MIMO Networks. This protocol doesn’t recognize cells, as shown by its name, even though a significant number of APs operate on the same frequency/time resources. Connection from multiple distributed access points through joint signal processing is called Cell-Free Massive MIMO. The Cell-Free Massive MIMO System, a contrast between Cell-Free Massive MIMO Systems and Distributed Massive MIMO, the prime focus in this thesis is on Cell-free Massive MIMO and, along with this discussion, on Cell-free Massive MIMO signal processing, Channel Estimation, Uplink Signal Detection, Cumulative Distribution, Spectral Efficiency & Ubiquitous Cell-Free Massive MIMO Model. Ubiquitous Cell-free Massive MIMO contributes to a Massive MIMO system, a distributed system that implements consistent user-centre distribution to solve that constraint of mobile phone interferences as well as to introduce macro-diversity. We investigated the Cell Radius at different locations in CDF with Spectral Efficiency [bits/s/hertz]. Cell-Free Massive MIMO is an evidence-based preventive of massive MIMOs with distributed high percentage APs that serve even lower margins. The cell-free model is not segregated into cells and any individual is concurrently represented by every Access point.
2

Stepanets, I., and G. Fokin. "FEATURES OF MASSIVE MIMO IN 5G NETWORKS." LastMile, no. 1 (2018): 46–52. http://dx.doi.org/10.22184/2070-8963.2018.70.1.46.52.

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3

Jang, Jeong-Uk, Jin-Hyuk Kim, and Cheol Mun. "Analysis of Massive MIMO Wireless Channel Characteristics." Journal of Korea Information and Communications Society 38B, no. 3 (March 29, 2013): 216–21. http://dx.doi.org/10.7840/kics.2013.38b.3.216.

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4

Kim, Yongok, and Sooyong Choi. "Performance Analysis of Massive MIMO Systems According to DoF." Journal of Korean Institute of Communications and Information Sciences 40, no. 11 (November 30, 2015): 2145–47. http://dx.doi.org/10.7840/kics.2015.40.11.2145.

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5

Jang, Seokju, Han-Bae Kong, and Inkyu Lee. "Pilot Assignment Algorithm for Uplink Massive MIMO Systems." Journal of Korean Institute of Communications and Information Sciences 40, no. 8 (August 31, 2015): 1485–91. http://dx.doi.org/10.7840/kics.2015.40.8.1485.

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6

Kim, Dowu, Seokjae Moon, and Jang-Won Lee. "Semi-Orthogonal Random Access for mMTC in Massive MIMO Systems." Journal of Korean Institute of Communications and Information Sciences 46, no. 7 (July 31, 2021): 1164–72. http://dx.doi.org/10.7840/kics.2021.46.7.1164.

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7

Chung, Jinjoo, Yonghee Han, and Jungwoo Lee. "Adaptive Channel Estimation Techniques for FDD Massive MIMO Systems." Journal of Korean Institute of Communications and Information Sciences 40, no. 7 (July 31, 2015): 1239–47. http://dx.doi.org/10.7840/kics.2015.40.7.1239.

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8

Chataut, Robin, and Robert Akl. "Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction." Sensors 20, no. 10 (May 12, 2020): 2753. http://dx.doi.org/10.3390/s20102753.

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The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.
9

Ohgane, Takeo, Toshihiko Nishimura, and Yasutaka Ogawa. "4. Massive MIMO." Journal of the Institute of Image Information and Television Engineers 70, no. 1 (2016): 17–22. http://dx.doi.org/10.3169/itej.70.17.

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10

Hwang, Inho, Han Park, and Jeong Lee. "LDPC Coded Massive MIMO Systems." Entropy 21, no. 3 (February 27, 2019): 231. http://dx.doi.org/10.3390/e21030231.

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We design a coded massive multiple-input multiple-output (MIMO) system using low-density parity-check (LDPC) codes and iterative joint detection and decoding (JDD) algorithm employing a low complexity detection. We introduce the factor graph representation of the LDPC coded massive MIMO system, based on which the message updating rule in the JDD is defined. We devise a tool for analyzing extrinsic information transfer (EXIT) characteristics of messages flowing in the JDD and the three-dimensional (3-D) EXIT chart provides a visualization of the JDD behavior. Based on the proposed 3-D EXIT analysis, we design jointly the degree distribution of irregular LDPC codes and the JDD strategy for the coded massive MIMO system. The JDD strategy was determined to achieve a higher error correction capability with a given amount of computational complexity. It was observed that the coded massive MIMO system equipped with the proposed LDPC codes and the proposed JDD strategy has lower bit error rate than conventional LDPC coded massive MIMO systems.
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Статті в журналах Дисертації Книги

Дисертації з теми "Massives MIMO":

1

Ladaycia, Abdelhamid. "Annulation d’interférences dans les systèmes MIMO et MIMO massifs (Massive MIMO)." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCD037.

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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
2

Karlsson, Marcus. "Aspects of Massive MIMO." Licentiate thesis, Linköpings universitet, Kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132718.

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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.
3

Becirovic, Ema. "On Massive MIMO for Massive Machine-Type Communications." Licentiate thesis, Linköpings universitet, Kommunikationssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162586.

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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.
4

Wannas, Hussain. "Full Duplex Multiuser MIMO with Massive Arrays." Thesis, Linköpings universitet, Institutionen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105268.

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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.
5

Alnajjar, Khawla. "Receiver Design for Massive MIMO." Thesis, University of Canterbury. Electrical and Computer Engineering, 2015. http://hdl.handle.net/10092/10517.

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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.
6

Negrão, João Lucas. "Efficient detection : from conventional Mimo to massive Mimo communication systems." Universidade Estadual de Londrina. Centro de Tecnologia e Urbanismo. Programa de Pós-Graduação em Engenharia Elétrica, 2018. http://www.bibliotecadigital.uel.br/document/?code=vtls000218370.

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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.
7

ORTEGA, ALVARO JAVIER. "SIGNAL DETECTION IN MASSIVE MIMO SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26176@1.

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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.
8

Payami, Sohail. "Hybrid beamforming for massive MIMO systems." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/842311/.

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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.
9

Ngo, Hien Quoc. "Massive MIMO: Fundamentals and System Designs." Doctoral thesis, Linköpings universitet, Kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112780.

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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.
10

Mursia, Placido. "Multi-antenna methods for scalable beyond-5G access networks." Thesis, Sorbonne université, 2021. http://www.theses.fr/2021SORUS532.

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L’augmentation exponentielle des équipements d’utilisateurs sans fil (UEs) et des services des réseaux associés aux déploiements actuels de cinquième génération (5G) pose plusieurs défis de conception sans précédent qui doivent être résolus avec l’avènement des futurs réseaux au-delà de la 5G. Plus précisément, la demande croissante de débits de données élevés ainsi que la nécessité de desservir un grand nombre d’appareils hétérogènes, allant des téléphones mobiles classiques aux objets connectés formant l’internet des objets (IoT), motivent l’étude de nouveaux schémas de traitement et de transmission du signal. À cet égard, les sorties multiples massives à entrées multiples (massive MIMO) sont une technologie d’accès bien établie, qui permet de desservir plusieurs dizaines d’UEs en utilisant lesmêmes ressources temps-fréquence au moyen de techniques de formation de faisceau hautement directionnelles. Cependant, le massive MIMO présente des problèmes d’évolutivité dans les scénarios accès massif où la population UE est composée d’un grand nombre de périphériques hétérogènes. En effet, si la disponibilité d’un grand nombre d’antennes dans les émetteurs-récepteurs massive MIMO apporte des gains de performances substantiels, elle augmente également considérablement la surcharge et la complexité du système. Plus précisément, la dimensionnalité élevée des canaux nécessite l’allocation de ressources temps-fréquence considérables pour acquérir les informations d’état de canal (CSI) et se traduit par de grandes opérations matricielles pour construire des précodeurs/décodeurs. De plus, dans le contexte de communications de multidiffusion comme, par exemple, la mise en cache périphérique sans fil ou la diffusion de messages critiques pour la mission, les techniques d’antennes multiples conventionnelles présentent des taux de disparition lorsque le nombre d’UEs augmente même dans le régime d’antenne massif. Enfin, le grand nombre de chaînes de radiofréquences (RF) associées aux émetteurs-récepteurs massive MIMO, qui sont utilisés pour contrer les pertes de propagation dans des environnements difficiles tels que, par exemple, à des fréquences d’ondes millimétriques (mmWave), se heurte au budget de puissance limité des appareils IoT. Dans cette thèse, nous proposons de nouvelles méthodes à antennes multiples évolutives pour l’amélioration des performances dans les scénarios d’intérêt susmentionnés. Plus précisément, nous décrivons le rôle fondamental joué par le CSI statistique qui peut être mis à profit pour réduire à la fois la complexité et la surcharge pour l’acquisition de CSI et pour la suppression des interférences multi-utilisateurs. En effet, lorsque les UEs sont équipés au moins de duex antennes, leurs propriétés de sélectivité spatiale peuvent être exploitées pour imposer une orthogonalité statistique parmi les transmissions interférentes. De plus, nous exploitons les communications de périphérique à périphérique (D2D) pour surmonter le goulot d’étranglement fondamental de la multidiffusion conventionnelle. En particulier, nous exploitons les capacités de précodage d’un émetteur multi-antennes pour sélectionner soigneusement les UEs dans des conditions de canal favorables, qui à leur tour agissent comme des relais opportunistes et retransmettent le message via les liaisons D2D. Enfin, dans le cadre des communications mmWave, nous explorons les avantages des surfaces intelligentes reconfigurables (RISs) récemment proposées, qui sont un catalyseur clé de l’innovation grâce à leur structure intrinsèquement passive qui permet de contrôler l’environnement de propagation et de contrer efficacement les pertes de propagation. En particulier, nous utilisons la formation de faisceaux passive au niveau du RIS, c’est-à-dire sans aucune dépense d’énergie significative, ainsi que la formation de faisceaux active conventionnelle au niveau de l’émetteur pour augmenter considérablement les performances du réseau
The exponential increase of wireless user equipments (UEs) and network services associated with current 5G deployments poses several unprecedented design challenges that need to be addressed with the advent of future beyond-5G networks and novel signal processing and transmission schemes. In this regard, massive MIMO is a well-established access technology, which allows to serve many tens of UEs using the same time-frequency resources. However, massive MIMO exhibits scalability issues in massive access scenarios where the UE population is composed of a large number of heterogeneous devices. In this thesis, we propose novel scalable multiple antenna methods for performance enhancement in several scenarios of interest. Specifically, we describe the fundamental role played by statistical channel state information (CSI) that can be leveraged for reduction of both complexity and overhead for CSI acquisition, and for multiuser interference suppression. Moreover, we exploit device-to-device communications to overcome the fundamental bottleneck of conventional multicasting. Lastly, in the context of millimiter wave communications, we explore the benefits of the recently proposed reconfigurable intelligent surfaces (RISs). Thanks to their inherently passive structure, RISs allow to control the propagation environment and effectively counteract propagation losses and substantially increase the network performance
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Книги з теми "Massives MIMO":

1

Cheng, Xiang, Shijian Gao, and Liuqing Yang. mmWave Massive MIMO Vehicular Communications. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-97508-1.

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Yang, Howard H., and Tony Q. S. Quek. Massive MIMO Meets Small Cell. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43715-6.

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3

Liu, Leibo, Guiqiang Peng, and Shaojun Wei. Massive MIMO Detection Algorithm and VLSI Architecture. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6362-7.

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4

Le-Ngoc, Tho, and Ruikai Mai. Hybrid Massive MIMO Precoding in Cloud-RAN. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02158-0.

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5

Zhao, Long, Hui Zhao, Kan Zheng, and Wei Xiang. Massive MIMO in 5G Networks: Selected Applications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68409-3.

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6

mmWave Massive MIMO. Elsevier, 2017. http://dx.doi.org/10.1016/c2015-0-01250-3.

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7

Massive MIMO Systems. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03936-017-8.

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8

Hong, Yang, Erik G. Larsson, Thomas L. Marzetta, and Hien Quoc Ngo. Fundamentals of Massive MIMO. Cambridge University Press, 2016.

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9

Hong, Yang, Erik G. Larsson, Thomas L. Marzetta, and Hien Quoc Ngo. Fundamentals of Massive MIMO. Cambridge University Press, 2016.

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10

Hong, Yang, Erik G. Larsson, Thomas L. Marzetta, and Hien Quoc Ngo. Fundamentals of Massive MIMO. University of Cambridge ESOL Examinations, 2016.

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Частини книг з теми "Massives MIMO":

1

Larsson, Erik G., and Emil Björnson. "Massive MIMO." In Encyclopedia of Wireless Networks, 771–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_136.

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Larsson, Erik G., and Emil Björnson. "Massive MIMO." In Encyclopedia of Wireless Networks, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32903-1_136-1.

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3

Ngo, Hien Quoc. "Massive MIMO." In 5G and Beyond, 101–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58197-8_4.

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4

Vook, Frederick W., Amitava Ghosh, and Timothy A. Thomas. "Massive MIMO Communications." In Towards 5G, 342–64. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118979846.ch15.

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5

Van Chien, Trinh, and Emil Björnson. "Massive MIMO Communications." In 5G Mobile Communications, 77–116. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34208-5_4.

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6

Gregorio, Fernando, Gustavo González, Christian Schmidt, and Juan Cousseau. "Massive MIMO Systems." In Signal Processing Techniques for Power Efficient Wireless Communication Systems, 193–216. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32437-7_8.

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7

Zhao, Long, Hui Zhao, Kan Zheng, and Wei Xiang. "Massive MIMO Technology." In SpringerBriefs in Electrical and Computer Engineering, 7–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68409-3_2.

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8

Xu, Wei, Yongming Huang, and Ming Xiao. "Millimeter Wave Massive MIMO." In Encyclopedia of Wireless Networks, 830–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_114.

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9

Ngo, Hien Quoc. "Cell-Free Massive MIMO." In Encyclopedia of Wireless Networks, 165–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_137.

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10

Yang, Jie, Shi Jin, Chao-Kai Wen, and Tao Jiang. "Massive MIMO Channel Estimation." In Encyclopedia of Wireless Networks, 779–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_145.

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Тези доповідей конференцій з теми "Massives MIMO":

1

Lejosne, Yohan, Manijeh Bashar, Dirk Slock, and Yi Yuan-Wu. "From MU massive MISO to pathwise MU massive MIMO." In 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2014. http://dx.doi.org/10.1109/spawc.2014.6941308.

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Vinogradova, Julia, Emil Bjornson, and Erik G. Larsson. "Jamming Massive MIMO using Massive MIMO: Asymptotic separability results." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952798.

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Liu, Liang, and Wei Yu. "Massive device connectivity with massive MIMO." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006693.

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Kudathanthirige, Dhanushka, and Gayan Amarasuriya. "Distributed Massive MIMO Downlink." In ICC 2019 - 2019 IEEE International Conference on Communications (ICC). IEEE, 2019. http://dx.doi.org/10.1109/icc.2019.8761446.

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Larsson, Erik G. "Fundamentals of massive MIMO." In 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015. http://dx.doi.org/10.1109/spawc.2015.7226986.

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Bjornson, Emil. "Massive MIMO for 5G." In 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015. http://dx.doi.org/10.1109/spawc.2015.7226987.

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7

Kudathanthirige, Dhanushka, and Gayan Amarasuriya. "Massive MIMO NOMA Downlink." In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8647417.

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"Session TA6: Massive MIMO." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421221.

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9

"Session MA8a3: Massive MIMO." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869018.

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10

Muller, Ralf R., Mohammad A. Sedaghat, and Georg Fischer. "Load modulated massive MIMO." In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2014. http://dx.doi.org/10.1109/globalsip.2014.7032192.

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Звіти організацій з теми "Massives MIMO":

1

VanDyke, J. P., J. L. Tomkins, and M. D. Furnish. Measures of effectiveness for BMD mid-course tracking on MIMD massively parallel computers. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/83111.

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