Journal articles: 'Massive MIMO'

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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.

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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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Handel, Peter, and Daniel Ronnow. "MIMO and Massive MIMO Transmitter Crosstalk." IEEE Transactions on Wireless Communications 19, no. 3 (March 2020): 1882–93. http://dx.doi.org/10.1109/twc.2019.2959534.

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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.

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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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Dutta, Ragit. "Performance Analysis of Massive MIMO under pilot contamination." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012051. http://dx.doi.org/10.1088/1742-6596/2327/1/012051.

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Abstract 5g is fast becoming a reality in the modern world. Telecom companies all around the world are already rolling out 5G in various parts of the world. As of now there are 3 key technologies that will enable us to implement 5G in our surroundings. Massive MIMO is one of them. Massive MIMO focuses on the aspect of improving the spectral efficiency. Tremendous data rates are achievable with spectral efficiency. However there are several bottlenecks in the implementation of Massive MIMO. Pilot Contamination is one of the most prominent issues in massive MIMO. This paper is about addressing the issue of Pilot Contamination in Massive MIMO. This paper also focuses on the various mitigation schemes proposed by researchers in the past.

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Marzetta, Thomas L. "Massive MIMO: An Introduction." Bell Labs Technical Journal 20 (2015): 11–22. http://dx.doi.org/10.15325/bltj.2015.2407793.

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Liang, Ning, and Wenyi Zhang. "Mixed-ADC Massive MIMO." IEEE Journal on Selected Areas in Communications 34, no. 4 (April 2016): 983–97. http://dx.doi.org/10.1109/jsac.2016.2544604.

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Choudhury, P. K., and M. Abou El-Nasr. "Massive MIMO toward 5G." Journal of Electromagnetic Waves and Applications 34, no. 9 (June 12, 2020): 1091–94. http://dx.doi.org/10.1080/09205071.2020.1783825.

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Druzhinina, N. S., and I. M. Daudov. "Analysis of the massive MIMO technology." Journal of Physics: Conference Series 2061, no. 1 (October 1, 2021): 012094. http://dx.doi.org/10.1088/1742-6596/2061/1/012094.

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Abstract The article discusses the features of the Massive MIMO technology, the structure of the antenna array, as well as the advantages and example of using the massive MIMO system. The use of Massive MIMO opens up new opportunities and makes a significant contribution to achieving the stated requirements for the further evolution of LTE and 5G.

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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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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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Salsabila, Salwa, Rina Pudjiastuti, Levy Olivia Nur, Harfan Hian Ryanu, and Bambang Setia Nugroho. "Scalable modular massive MIMO antenna of rectangular truncated corner patch antenna and circular slotted X patch antenna for 5G antenna communication." JURNAL INFOTEL 15, no. 3 (August 28, 2023): 274–80. http://dx.doi.org/10.20895/infotel.v15i3.962.

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Massive MIMO Antenna Design results in a very large antenna size that hinders the design process. The arrangement of Massive MIMO Antennas which consists of many antenna elements is a challenge in the design process due to the limited capability of the simulation software and the complicated process. Thus, a scalability technique is used to predict the specification results produced by a Massive MIMO Antenna array with a certain configuration based on a simple MIMO Antenna array with a 2x2, 4x4, 8x8, 16x16 MIMO element configuration scheme, etc. exponential increments. This research will discuss the scaling process to predict the specifications of a Massive MIMO Antenna array. The designed MIMO antenna arrangement is based on the design of a rectangular antenna with a truncated corner and a circular antenna with an X slot for further design with various types of configurations that work at a frequency of 3.5 GHz.

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Hu, Qiang, Meixiang Zhang, and Renzheng Gao. "Key Technologies in Massive MIMO." ITM Web of Conferences 17 (2018): 01017. http://dx.doi.org/10.1051/itmconf/20181701017.

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The explosive growth of wireless data traffic in the future fifth generation mobile communication system (5G) has led researchers to develop new disruptive technologies. As an extension of traditional MIMO technology, massive MIMO can greatly improve the throughput rate and energy efficiency, and can effectively improve the link reliability and data transmission rate, which is an important research direction of 5G wireless communication. Massive MIMO technology is nearly three years to get a new technology of rapid development and it through a lot of increasing the number of antenna communication, using very duplex communication mode, make the system spectrum efficiency to an unprecedented height.

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Ibrahim, Sura Khalil, Mandeep Jit Singh, Samir Salem Al-Bawri, Husam Hamid Ibrahim, Mohammad Tariqul Islam, Md Shabiul Islam, Ahmed Alzamil, and Wazie M. Abdulkawi. "Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review." Nanomaterials 13, no. 3 (January 28, 2023): 520. http://dx.doi.org/10.3390/nano13030520.

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Massive multiple-input multiple-output (mMIMO) is a wireless access technique that has been studied and investigated in response to the worldwide bandwidth demand in the wireless communication sector (MIMO). Massive MIMO, which brings together antennas at the transmitter and receiver to deliver excellent spectral and energy efficiency with comparatively simple processing, is one of the main enabling technologies for the upcoming generation of networks. To actualize diverse applications of the intelligent sensing system, it is essential for the successful deployment of 5G—and beyond—networks to gain a better understanding of the massive MIMO system and address its underlying problems. The recent huge MIMO systems are highlighted in this paper’s thorough analysis of the essential enabling technologies needed for sub-6 GHz 5G networks. This article covers most of the critical issues with mMIMO antenna systems including pilot realized gain, isolation, ECC, efficiency, and bandwidth. In this study, two types of massive 5G MIMO antennas are presented. These types are used depending on the applications at sub-6 GHz bands. The first type of massive MIMO antennas is designed for base station applications, whereas the most recent structures of 5G base station antennas that support massive MIMO are introduced. The second type is constructed for smartphone applications, where several compact antennas designed in literature that can support massive MIMO technology are studied and summarized. As a result, mMIMO antennas are considered as good candidates for 5G systems.

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Et.al, G. Jagga Rao. "Milli meter Wave MIMO-OFDMA Scheme with MMSE-based VEMF in 6G Wireless Technology." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 4701–7. http://dx.doi.org/10.17762/turcomat.v12i3.1890.

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Millimetre Wave (MmWave) massive multiple-input multiple-output (MmWave-massive-MIMO) has developed as beneficial for gigabit-per-second data broadcast into 6G digitized wireless technology. The collection of low-rate and energy-efficient (EE) types of machinery, low power consumptions, multi-bit quantized massive MIMO-Orthogonal Frequency Division Multiplexing Access (OFDMA) structure have been planned for the receiver manner. The main concentration effort is the minimization of a state-of-the-art pilot-symbol quantized (PSQ) massive MIMO-OFDMA system (m-MIMO-OFDM-S). Accordingly, in this analysis, by minimizing many advantages of the Variational Estimated Message Fleeting (VEMF) algorithm. A modified low complexity manner VEMF algorithm is invented for the utilization of the ASQ-m-MIMO-OFDM-S structure. Hence, two new modules improve the energy efficiency and spectrum efficiency for wireless smart 6G technology of pilot bits allocation process for MmWave connections of the hybrid MIMO-OFDM receiver structural design. Several technologies such as massive MIMO-OFDMA, 3GPP & 4G& 5G technology, the device to device communication (D2D), GREEN communication have increasingly important consideration in assisting spectrum consumption along with power consumption during simulations. The proposed VEMF algorithm has achieved higher capacity, sum rate, Energy Efficiency (EE), and throughput for the receiver section. Finally, we present a greater number of user's data transmissions MmWave-massive-MIMO-OFDMA system.

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Vaigandla, Karthik Kumar, and Dr N. Venu. "Survey on Massive MIMO: Technology, Challenges, Opportunities and Benefits." YMER Digital 20, no. 11 (November 20, 2021): 271–82. http://dx.doi.org/10.37896/ymer20.11/25.

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Wireless communication technologies have been studied and explored in response to the global shortage of bandwidth in the field of wireless access. Next-generation networks will be enabled by massive MIMO. Using relatively simple processing, it provides high spectral and energy efficiency by combining antennas at the receiver and transmitter. This paper discusses enabling technologies, benefits, and opportunities associated with massive MIMO, and all the fundamental challenges. Global enterprises, research institutions, and universities have focused on researching the 5G mobile communication network. Massive MIMO technologies will utilize simpler and linear algorithms for beam forming and decoding. As part of future 5G, massive MIMO technology will be used to increase the efficiency of spectrum utilization and channel capacity. The paper then summarizes the technologies that are used in massive MIMO system, including channel estimation, pre-coding, and signal detection.

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Shamsan, Z. A. "Statistical Analysis of 5G Channel Propagation using MIMO and Massive MIMO Technologies." Engineering, Technology & Applied Science Research 11, no. 4 (August 21, 2021): 7417–23. http://dx.doi.org/10.48084/etasr.4264.

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Multiple Input Multiple Output (MIMO) and massive MIMO technologies play a significant role in mitigating five generation (5G) channel propagation impairments. These impairments increase as frequency increases, and they become worse at millimeter-waves (mmWaves). They include difficulties of material penetration, Line-of-Sight (LoS) inflexibility, small cell coverage, weather circumstances, etc. This paper simulates the 5G channel at the E-band frequency using the Monte Carlo approach-based NYUSIM tool. The urban microcell (UMi) is the communication environment of this simulation. Both MIMO and massive MIMO use uniformly spaced rectangular antenna arrays (URA). This study investigates the effects of MIMO and massive MIMO on LOS and Non-LOS (NLOS) environments. The simulations considered directional and omnidirectional antennas, the Power Delay Profile (PDP), Root Mean Square (RMS) delay spread, and small-scale PDP for both LOS and NLOS environments. As expected, the wide variety of the results showed that the massive MIMO antenna outperforms the MIMO antenna, especially in terms of the signal power received at the end-user and for longer path lengths.

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Waseem, Athar, Aqdas Naveed, Sardar Ali, Muhammad Arshad, Haris Anis, and Ijaz Mansoor Qureshi. "Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems." Wireless Communications and Mobile Computing 2019 (May 27, 2019): 1–15. http://dx.doi.org/10.1155/2019/6374764.

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Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

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Han, Tongzhou, and Danfeng Zhao. "Energy Efficiency of User-Centric, Cell-Free Massive MIMO-OFDM with Instantaneous CSI." Entropy 24, no. 2 (February 3, 2022): 234. http://dx.doi.org/10.3390/e24020234.

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In the user-centric, cell-free, massive multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, a large number of deployed access points (APs) serve user equipment (UEs) simultaneously, using the same time–frequency resources, and the system is able to ensure fairness between each user; moreover, it is robust against fading caused by multi-path propagation. Existing studies assume that cell-free, massive MIMO is channel-hardened, the same as centralized massive MIMO, and these studies address power allocation and energy efficiency optimization based on the statistics information of each channel. In cell-free, massive MIMO systems, especially APs with only one antenna, the channel statistics information is not a complete substitute for the instantaneous channel state information (CSI) obtained via channel estimation. In this paper, we propose that energy efficiency is optimized by power allocation with instantaneous CSI in the user-centric, cell-free, massive MIMO-OFDM system, and we consider the effect of CSI exchanging between APs and the central processing unit. In addition, we design different resource block allocation schemes, so that user-centric, cell-free, massive MIMO-OFDM can support enhanced mobile broadband (eMBB) for high-speed communication and massive machine communication (mMTC) for massive device communication. The numerical results verify that the proposed energy efficiency optimization scheme, based on instantaneous CSI, outperforms the one with statistical information in both scenarios.

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Kulshreshtha, Garima, and Usha Chauhan. "Implementation of Massive Multiple-Input Multiple-Output (MIMO) 5G Communication System using Modified Least-Mean-Square (LMS) Adaptive Filters Algorithm." International Journal of Electrical and Electronics Research 12, no. 3 (August 10, 2024): 905–18. http://dx.doi.org/10.37391/ijeer.120322.

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The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.

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Jiang, Bin, Bowen Ren, Yufei Huang, Tingting Chen, Li You, and Wenjin Wang. "Energy Efficiency and Spectral Efficiency Tradeoff in Massive MIMO Multicast Transmission with Statistical CSI." Entropy 22, no. 9 (September 18, 2020): 1045. http://dx.doi.org/10.3390/e22091045.

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As the core technology of 5G mobile communication systems, massive multi-input multi-output (MIMO) can dramatically enhance the energy efficiency (EE), as well as the spectral efficiency (SE), which meets the requirements of new applications. Meanwhile, physical layer multicast technology has gradually become the focus of next-generation communication technology research due to its capacity to efficiently provide wireless transmission from point to multipoint. The availability of channel state information (CSI), to a large extent, determines the performance of massive MIMO. However, because obtaining the perfect instantaneous CSI in massive MIMO is quite challenging, it is reasonable and practical to design a massive MIMO multicast transmission strategy using statistical CSI. In this paper, in order to optimize the system resource efficiency (RE) to achieve EE-SE balance, the EE-SE trade-offs in the massive MIMO multicast transmission are investigated with statistical CSI. Firstly, we formulate the eigenvectors of the RE optimization multicast covariance matrices of different user terminals in closed form, which illustrates that in the massive MIMO downlink, optimal RE multicast precoding is supposed to be done in the beam domain. On the basis of this viewpoint, the optimal RE precoding design is simplified into a resource efficient power allocation problem. Via invoking the quadratic transform, we propose an iterative power allocation algorithm, which obtains an adjustable and reasonable EE-SE tradeoff. Numerical simulation results reveal the near-optimal performance and the effectiveness of our proposed statistical CSI-assisted RE maximization in massive MIMO.

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C, Saravanakumar, Allanki Sanyasi Rao, Harini C, and Saravanan Velusamy. "ADVANCEMENT IN LOCALIZATION TECHNIQUES USING PRECODERS FOR ULTRA WIDE-BAND SYSTEMS." ICTACT Journal on Communication Technology 14, no. 3 (September 1, 2023): 2982–87. http://dx.doi.org/10.21917/ijct.2023.0443.

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In the era of rapidly expanding wireless communication systems, the demand for high-performance, low-latency, and energy-efficient solutions is paramount. One technology that has emerged as a transformative force in addressing these requirements is Massive Multiple-Input Multiple-Output (Massive MIMO) precoding. This abstract delves into the key aspects of Massive MIMO precoding, highlighting its role in enhancing spectral efficiency, mitigating interference, and improving the overall performance of wireless networks. Massive MIMO precoding leverages a substantial number of antennas at the transmitter, allowing for the creation of highly focused spatial beams. These beams can be dynamically optimized to cater to the specific requirements of individual users or devices, maximizing the spectral efficiency by spatially multiplexing multiple streams. This technique offers significant advantages in terms of increasing network capacity and achieving higher data rates, especially in dense network scenarios. Furthermore, Massive MIMO precoding excels in interference mitigation. By spatially directing signals toward intended recipients and steering nulls towards interferers, it reduces the impact of co-channel interference, enhancing network reliability and quality of service. This is particularly valuable in scenarios where network congestion and interference pose significant challenges, such as urban environments and crowded event venues. The research delves into the role of Massive MIMO precoding in improving the signal-to-noise ratio, which directly translates to extended coverage areas and reduced power consumption. Additionally, we explore the implications of Massive MIMO precoding in enabling efficient communication in massive Internet of Things (IoT) deployments and its potential for 5G and beyond. Massive MIMO precoding is poised to reshape the wireless communication landscape. It promises to deliver unprecedented gains in spectral efficiency, interference management, and energy efficiency. As the demand for high-speed, reliable, and ubiquitous connectivity continues to surge, this research plays the pivotal role that Massive MIMO precoding plays in meeting these demands, ushering in a new era of wireless communication.

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Hong, Jun-Ki. "Performance Analysis of Dual-Polarized Massive MIMO System with Human-Care IoT Devices for Cellular Networks." Journal of Sensors 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/3604520.

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The performance analysis of the dual-polarized massive multiple-input multiple-output (MIMO) system with Internet of things (IoT) devices is studied when outdoor human-care IoT devices are connected to a cellular network via a dual-polarized massive MIMO system. The research background of the performance analysis of dual-polarized massive MIMO system with IoT devices is that recently the data usage of outdoor human-care IoT devices has increased. Therefore, the outdoor human-care IoT devices are necessary to connect with 5G cellular networks which can expect 1000 times higher performance compared with 4G cellular networks. Moreover, in order to guarantee the safety of the patient for emergency cases, a human-care Iot device must be connected to cellular networks which offer more stable communication for outdoors compared to short-range communication technologies such as Wi-Fi, Zigbee, and Bluetooth. To analyze the performance of the dual-polarized massive MIMO system for human-care IoT devices, a dual-polarized MIMO spatial channel model (SCM) is proposed which considers depolarization effect between the dual-polarized transmit-antennas and the receive-antennas. The simulation results show that the performance of the dual-polarized massive MIMO system is improved about 16% to 92% for 20 to 150 IoT devices compared to conventional single-polarized massive MIMO system for identical size of the transmit array.

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Liao, Chengjian, Kui Xu, Xiaochen Xia, Wei Xie, Nan Sha, Lihua Chen, and Ningsong Liu. "Geometry-Based Stochastic Model and Statistical Characteristic Analysis of Cell-Free Massive MIMO Channels." Security and Communication Networks 2022 (October 17, 2022): 1–19. http://dx.doi.org/10.1155/2022/4730044.

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Cell-free massive multi-input multi-output (MIMO) systems exhibit many characteristics that differ from those of traditional centralized massive MIMO systems, and there are still research gaps in the modeling of cell-free massive MIMO channels. In this paper, a geometry-based stochastic model (GBSM) that combines the double-ring model and hemisphere model to comprehensively consider the distribution of scatterers in the environment was proposed for cell-free massive MIMO channels. Combined with the line-of-sight (LoS) path, single scattering path, and double scattering path components, the channel matrix between the access points (APs) and the user was derived. The proposed model fully considers geometric parameters such as the arrival/departure direction, elevation angle, time delay, and distance, which can accurately characterize the channel. Then, we proved that the traditional channel model, standard block-fading model, and spatial basis expansion model (SBEM) adopted in a cell-free massive MIMO system could not describe the nonstationarity in the space, time, and frequency domains, whereas the proposed GBSM could. Statistical characteristics of the channel were analyzed, including the space cross-correlation function (CCF), time autocorrelation function (ACF), Doppler power spectral density (PSD), level crossing rate (LCR), and average fade duration (AFD). Then, we investigated the proposed model by simulating the space CCF, time ACF, Doppler PSD, LCR, and AFD under the conditions of two different scatterer densities. Through simulations and analyses, some new features of cell-free massive MIMO channels were identified, providing a theoretical basis for in-depth research on cell-free massive MIMO systems. Finally, the measurement-based scenario and the WINNER II channel model are compared to demonstrate that the GBSM is more practical to characterize real cell-free massive MIMO channels.

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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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Li, Ning, and Pingzhi Fan. "Distributed Cell-Free Massive MIMO Versus Cellular Massive MIMO Under UE Hardware Impairments." Chinese Journal of Electronics 33, no. 5 (September 2024): 1274–85. http://dx.doi.org/10.23919/cje.2023.00.045.

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Weng, Jialai, Xiaoming Tu, Zhihua Lai, Sana Salous, and Jie Zhang. "Indoor Massive MIMO Channel Modelling Using Ray-Launching Simulation." International Journal of Antennas and Propagation 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/279380.

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Massive multi-input multioutput (MIMO) is a promising technique for the next generation of wireless communication networks. In this paper, we focus on using the ray-launching based channel simulation to model massive MIMO channels. We propose one deterministic model and one statistical model for indoor massive MIMO channels, both based on ray-launching simulation. We further propose a simplified version for each model to improve computational efficiency. We simulate the models in indoor wireless network deployment environments and compare the simulation results with measurements. Analysis and comparison show that these ray-launching based simulation models are efficient and accurate for massive MIMO channel modelling, especially with application to indoor network planning and optimisation.

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Gani, Ahmad. "Implementation of Massive MIMO in 5G Networks: Strategy and Technical Studies in Indonesia." Indonesian Journal of Advanced Research 2, no. 3 (March 30, 2023): 189–200. http://dx.doi.org/10.55927/ijar.v2i3.3563.

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This study aims to examine the implementation of Massive MIMO (Multiple-Input Multiple-Output) in the 5G network and its implementation strategy in Indonesia. The methodology used includes literature study, field data analysis, and simulation. This study found that Massive MIMO can increase the capacity, speed, and spectrum efficiency of 5G networks. Implementation of this technology in Indonesia requires a strategy that includes optimizing infrastructure, regulation, and cooperation between stakeholders. The research results show that the implementation of Massive MIMO in the 5G network in Indonesia will provide significant benefits for users and the telecommunication industry. The conclusion drawn is that Massive MIMO is a key technology in developing 5G networks in Indonesia and needs full support from the government, operators, and related industries.

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Antsa Salomà El Salam Andriamihaja. "PERFORMANCE EVALUATION OF A MASSIVE MIMO M-MMSE SYSTEM IN TERMS OF ENERGY EFFICIENCY BASED ON THE POWER CIRCUIT CONSUMPTION MODEL (PC)." International Journal of Innovations in Engineering Research and Technology 11, no. 2 (February 15, 2024): 33–43. http://dx.doi.org/10.26662/ijiert.v11i2.pp33-43.

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The implementation of massive MIMO technology presents a significant boost in throughput mobile networks. However, this technological advancement comes increases energy consumption. The amalgamation of Massive MIMO with M-MMSE serves as a strategic solution, effectively mitigating energy consumption while delivering substantial throughput and improved Energy Efficiency (EE) compared to alternative techniques. Our study delves into the Energy Efficiency of massive MIMO using the Power Circuit Consumption (PC) model, providing valuable insights into its performance. This analytical approach not only enables a reduction in energy usage but also facilitates a noteworthy enhancement in Spectral efficiency (SE) and EE concurrently. This concurrent improvement addresses the challenge posed by the initial surge in energy consumption associated with massive MIMO deployment, ensuring a sustainable and efficient integration into mobile networks.

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Bai, Yanna, Wei Chen, Bo Ai, and Zhangdui Zhong. "Contention-based nonorthogonal massive access with massive MIMO." China Communications 17, no. 11 (November 2020): 79–90. http://dx.doi.org/10.23919/jcc.2020.11.007.

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Liang, Shiyu, Wei Chen, Zhongwen Sun, Ao Chen, and Bo Ai. "Cluster-based massive access for massive MIMO systems." China Communications 21, no. 1 (January 2024): 24–33. http://dx.doi.org/10.23919/jcc.fa.2023-0380.202401.

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Abdul-Hadi, Alaa M., Marwah Abdulrazzaq Naser, Muntadher Alsabah, Sadiq H. Abdulhussain, and Basheera M. Mahmmod. "Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models." PeerJ Computer Science 8 (June 21, 2022): e1017. http://dx.doi.org/10.7717/peerj-cs.1017.

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Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently accurate DL CSI estimation. Specifically, to reduce the DL CSI estimation overhead, the training sequence is designed based on the eigenvectors of the transmit correlation matrix. To this end, the achievable sum rate (ASR) maximization and the mean square error (MSE) of CSI estimation with short CT are investigated using the proposed training sequence design. Furthermore, this article examines the effect of channel hardening in an FDD massive-MIMO system. The results demonstrate that in high correlation scenarios, a large loss in channel hardening is obtained. The results reveal that increasing the correlation level reduces the MSE but does not increase the ASR. However, exploiting the spatial correction structure is still very essential for the FDD massive-MIMO systems under limited CT. This finding holds for all the physical correlation models considered.

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Galih, Savitri, ., and . "Low Complexity Interference Alignment for Distributed Large-Scale MIMO Hardware Architecture and Implementation for 5G Communication." International Journal of Engineering & Technology 7, no. 4.33 (December 9, 2018): 208. http://dx.doi.org/10.14419/ijet.v7i4.33.23561.

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Massive MIMO or Large Scale MIMO is a promising solution for achieving superior data rates in 5G communication systems. However, it has limitation in term of scalability and coverage for users that has highly spatial separation. Distributed massive MIMO is expected to enhance these drawbacks. One main problem arises in this scheme is the MIMO interference channel condition that can be copied by interference alignment algorithm. The main consideration for interference alignment algorithm in distributed Massive MIMO is to achieve low complexity precoding to eliminate interference channel condition and to design efficient hardware architecture for its implementation. Previous research regarding IA for Distributed Massive MIMO indicate that the complexity issues is still not widely discussed. This paper proposed the low complexity IA scheme for large scale MIMO system based on limited interferer and the implementation of low cost interference alignment and wireless synchronization for distributed MIMO using software defined radio hardware. From the simulation result, it shows that limited interferer IA algorithm achieve acceptable BER performance, i.e. in order of 10-3. The hardware implementation of the IA precoding matrix computation is also discussed. Based on the experiment, it is show that the proposed algorithm and architecture achieved higher hardware performance compared to the linear IA.

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Onukwugha, Chinwe Gilean, Njoku, Donatus Onyedikachi, Jibiri, Janefrances Ebere, Dimoji, and Chigozie. "Bit Error Rate Analysis Of Multiuser Massive MIMO Wireless System Using Linear Precoding." International Journal of Advanced Networking and Applications 14, no. 04 (2023): 5517–22. http://dx.doi.org/10.35444/ijana.2023.14403.

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This paper presents bit error rate (BER) analysis of multiuser (MU) massive multiple input multiple out (MIMO) wireless system using linear precoding techniques. Considering the increasing demand for wireless communication that will provide seamless performance to meet users satisfaction, various precoding schemes have been developed and with massive MIMO projected to be a promising technology for 5G and next generation network. Therefore, this study was basically designed to examine the effect of linear zero forcing (ZF) and minimum mean square error (MMSE) precoders on BER performance of MU massive MIMO system with up to 32 base station antennas (BS) and communicating with up to 5 user mobile terminals (MTs). A model that describes downlink operation of MU massive MIMO wireless communication system with spatial multiplexing employing linear ZF and MMSE precoders such that each user is equipped with single antenna MT resulting in overall access points of 5 antennas was developed in MATLAB. Computer simulations revealed that ZF outperformed MMSE. This observation was validated by similar report on massive MIMO in previous study.

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Zheng, Kan, Suling Ou, and Xuefeng Yin. "Massive MIMO Channel Models: A Survey." International Journal of Antennas and Propagation 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/848071.

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The exponential traffic growth of wireless communication networks gives rise to both the insufficient network capacity and excessive carbon emissions. Massive multiple-input multiple-output (MIMO) can improve the spectrum efficiency (SE) together with the energy efficiency (EE) and has been regarded as a promising technique for the next generation wireless communication networks. Channel model reflects the propagation characteristics of signals in radio environments and is very essential for evaluating the performances of wireless communication systems. The purpose of this paper is to investigate the state of the art in channel models of massive MIMO. First, the antenna array configurations are presented and classified, which directly affect the channel models and system performance. Then, measurement results are given in order to reflect the main properties of massive MIMO channels. Based on these properties, the channel models of massive MIMO are studied with different antenna array configurations, which can be used for both theoretical analysis and practical evaluation.

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Hilme, Israa, and Ayad Atiyah Abdulkafi. "Energy-Efficient Massive MIMO Network." Tikrit Journal of Engineering Sciences 30, no. 3 (August 2, 2023): 1–8. http://dx.doi.org/10.25130/tjes.30.3.1.

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Massive Multiple-Input Multiple-Output (Massive MIMO) is widely regarded as a highly promising technology for the forthcoming generation of wireless systems. The massive MIMO implementation involves the integration of a substantial number of antenna elements into base stations (BSs) to enhance spectral efficiency (SE) and energy efficiency (EE). The energy efficiency (EE) of base stations (BSs) has become an increasingly important issue for telecommunications network operators due to the need to take care of profitability while simultaneously minimizing their detrimental effects on the environment and addressing economic challenges faced by wireless communication operators. In this paper, the EE of massive MIMO networks and the relationship between EE, SE, and other parameters like bandwidth (B), number of antennas (M), circuit power, and number of users’ equipment (K) are discussed and investigated. For a fixed circuit power (PFIX), simulation results showed that the EE could be increased by about 1.12 as the number of antennas was doubled. The findings in this work also indicated an almost linear relationship between maximum EE and optimal SE, with a massive increase in the number of antennas when the power consumed by each antenna (PBS) was included in circuit power. In addition, when considering the power consumed per user’s equipment (PUE) impact, the SE increased with the ratio (M/K), in which SE showed a cubic relationship against M/K. On the other hand, the EE increased with M/K ratio until M/K reached a specific value. The maximum EE (and hence optimum SE) was achieved by massive MIMO, where the number of antennas was three times the number of users. However, EE started degrading after this value, as the number of antennas was considered larger than the users’ and consumed more energy, resulting in EE degradation.

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Jit Singh, Mandeep Singh, Wan Syahrum Wan Saleh, Amer T. Abed, and Muhammad Ashraf Fauzi. "A Review on Massive MIMO Antennas for 5G Communication Systems on Challenges and Limitations." Jurnal Kejuruteraan 35, no. 1 (January 30, 2023): 95–103. http://dx.doi.org/10.17576/jkukm-2023-35(1)-09.

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High data rate transfers, high-definition streaming, high-speed internet, and the expanding of the infrastructure such as the ultra-broadband communication systems in wireless communication have become a demand to be considered in improving quality of service and increase the capacity supporting gigabytes bitrate. Massive Multiple-Input Multiple-Output (MIMO) systems technology is evolving from MIMO systems and becoming a high demand for fifth-generation (5G) communication systems and keep expanding further. In the near future, massive MIMO systems could be the main wireless systems of communications technology and can be considered as a key technology to the system in daily lives. The arrangement of the huge number of antenna elements at the base station (BS) for uplink and downlink to support the MIMO systems in increasing its capacity is called a Massive MIMO system, which refers to the vast provisioning of antenna elements at base stations over the number of the single antenna of user equipment. Massive MIMO depends on spatial multiplexing and diversity gain in serving users with simple processing signal of uplink and downlink at the BS. There are challenges in massive MIMO system even though it contains numerous number of antennas, such as channel estimation need to be accurate, precoding at the BS, and signal detection which is related to the first two items. On the other hand, in supporting wideband cellular communication systems and enabling low latency communications and multi-gigabit data rates, the Millimeter-wave (mmWave) technology has been utilized. Also, it is widely influenced the potential of the fifth-generation (5G) New Radio (NR) standard. This study was specifically review and compare on a few designs and methodologies on massive MIMO antenna communication systems. There are three limitations of those antennas were identified to be used for future improvement and to be proposed in designing the massive MIMO antenna systems. A few suggestions to improve the weaknesses and to overcome the challenges have been proposed for future considerations.

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Kei Sakaguchi, Takumi Yoneda, Masashi Iwabuchi, and Tomoki Murakami. "mmWave massive analog relay MIMO." ITU Journal on Future and Evolving Technologies 2, no. 6 (September 24, 2021): 43–55. http://dx.doi.org/10.52953/wzof2275.

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Millimeter-Wave (mmWave) communications are a key technology to realize ultra-high data rate and ultra-low latency wireless communications. Compared with conventional communication systems in the microwave band such as 4G/LTE, mmWave communications employ a higher frequency band which allows a wider bandwidth and is suitable for large capacity communications. It is expected to be applied to various use cases such as mmWave cellular networks and vehicular networks. However, due to the strong diffraction loss and the path loss in the mmWave band, it is difficult or even impossible to achieve high channel capacity for User Equipment (UE) located in Non-Line-Of-Sight (NLOS) environments. To solve the problem, the deployment of relay nodes has been considered. In this paper, we consider the use of massive analog Relay Stations (RSs) to relay the transmission signals. By relaying the signals by a large number of RSs, an artificial Multiple-Input Multiple-Output (MIMO) propagation environment can be formed, which enables mmWave MIMO communications to the NLOS environment. We describe a theoretical study of a massive relay MIMO system and extend it to include multi-hop relays. Simulations are conducted, and the numerical results show that the proposed system achieves high data rates even in a grid-like urban environment.

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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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Borges, David, Paulo Montezuma, Rui Dinis, and Marko Beko. "Massive MIMO Techniques for 5G and Beyond—Opportunities and Challenges." Electronics 10, no. 14 (July 13, 2021): 1667. http://dx.doi.org/10.3390/electronics10141667.

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Telecommunications have grown to be a pillar to a functional society and the urge for reliable and high throughput systems has become the main objective of researchers and engineers. State-of-the-art work considers massive Multiple-Input Multiple-Output (massive MIMO) as the key technology for 5G and beyond. Large spatial multiplexing and diversity gains are some of the major benefits together with an improved energy efficiency. Current works mostly assume the application of well-established techniques in a massive MIMO scenario, although there are still open challenges regarding hardware and computational complexities and energy efficiency. Fully digital, analog, and hybrid structures are analyzed and a multi-layer massive MIMO transmission technique is detailed. The purpose of this article is to describe the most acknowledged transmission techniques for massive MIMO systems and to analyze some of the most promising ones and identify existing problems and limitations.

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Yu, Yongzhi, Jianming Wang, and Limin Guo. "Multisegment Mapping Network for Massive MIMO Detection." International Journal of Antennas and Propagation 2021 (September 3, 2021): 1–7. http://dx.doi.org/10.1155/2021/9989634.

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The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.

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Yu, Xianglong, An-An Lu, Xiqi Gao, Geoffrey Ye Li, Guoru Ding, and Cheng-Xiang Wang. "HF Skywave Massive MIMO Communication." IEEE Transactions on Wireless Communications 21, no. 4 (April 2022): 2769–85. http://dx.doi.org/10.1109/twc.2021.3115820.

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Sun, Chen, Jiaheng Wang, Xiqi Gao, and Zhi Ding. "Networked Optical Massive MIMO Communications." IEEE Transactions on Wireless Communications 19, no. 8 (August 2020): 5575–88. http://dx.doi.org/10.1109/twc.2020.2994545.

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Marzetta, Thomas L., Giuseppe Caire, Merouane Debbah, I. Chih-Lin, and Saif K. Mohammed. "Special issue on Massive MIMO." Journal of Communications and Networks 15, no. 4 (August 2013): 333–37. http://dx.doi.org/10.1109/jcn.2013.000064.

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Bjornson, Emil, Jakob Hoydis, and Luca Sanguinetti. "Massive MIMO Has Unlimited Capacity." IEEE Transactions on Wireless Communications 17, no. 1 (January 2018): 574–90. http://dx.doi.org/10.1109/twc.2017.2768423.

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Garcia, Nil, Henk Wymeersch, Erik G. Larsson, Alexander M. Haimovich, and Martial Coulon. "Direct Localization for Massive MIMO." IEEE Transactions on Signal Processing 65, no. 10 (May 15, 2017): 2475–87. http://dx.doi.org/10.1109/tsp.2017.2666779.

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Yilmaz, Baki Berkay, and Alper T. Erdogan. "Compressed Training Based Massive MIMO." IEEE Transactions on Signal Processing 67, no. 5 (March 2019): 1191–206. http://dx.doi.org/10.1109/tsp.2018.2890374.

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Basnayaka, Dushyantha A., Marco Di Renzo, and Harald Haas. "Massive But Few Active MIMO." IEEE Transactions on Vehicular Technology 65, no. 9 (September 2016): 6861–77. http://dx.doi.org/10.1109/tvt.2015.2490548.

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Qiao, Deli, Haifeng Qian, and Geoffrey Ye Li. "Broadbeam for Massive MIMO Systems." IEEE Transactions on Signal Processing 64, no. 9 (May 2016): 2365–74. http://dx.doi.org/10.1109/tsp.2016.2521609.

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Journal articles on the topic 'Massive MIMO'

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