Статті в журналах: "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.

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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.

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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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11

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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12

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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13

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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14

Huang Jingze, 黄竞择, 梁旭文 Liang Xuwen та 谢卓辰 Xie Zhuochen. "基于混合波束赋形的毫米波大规模MIMO信道估计". Laser & Optoelectronics Progress 59, № 5 (2022): 0506002. http://dx.doi.org/10.3788/lop202259.0506002.

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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

Lee, Je-Woo, Young-Min Kim, Hee-Jun Ahn, and Een-Kee Hong. "Analysis of User Association Impact on User-Centric Cell-Free Massive MIMO." Journal of Korean Institute of Communications and Information Sciences 45, no. 11 (November 30, 2020): 2014–21. http://dx.doi.org/10.7840/kics.2020.45.11.2014.

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27

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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28

Tonin, Jean Marcel Faria, and Taufik Abrao. "Linear detectors and precoding methods for massive MIMO." Semina: Ciências Exatas e Tecnológicas 42, no. 2 (December 2, 2021): 209. http://dx.doi.org/10.5433/1679-0375.2021v42n2p209.

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Detection in multiple-input-multiple-output (MIMO) wireless communication systems is a crucial procedure in receivers since the multiple access transmission schemes generate interference due to the simultaneous transmission along with the several antennas, unlike single-input-single-output (SISO) transmission schemes. Precoding is a technique in MIMO systems used to mitigate the effects of the channel over the received signal. Hence, it is possible to adjust continuously the transmitted information to reverse the effect of the wireless channel at the receiver side. In this work, linear sub-optimal detectors and precoders for massive MIMO (M-MIMO) systems are implemented, analyzed, and compared in terms of performance-complexity trade-off. It is also being considered numerical results in both channel scenarios: a) receiver and transmitter have perfect channel state information (CSI); b) complex channel coefficients are estimated with different levels of inaccuracy. Monte-Carlo simulations (MCS) reveal that linear zero-forcing (ZF) and minimum mean squared error (MMSE) massive MIMO detectors result in a certain robustness against multi-user interference when operating under low and medium system loading, L = K/M, thanks to the favourable propagation phenomenon arising in massive MIMO systems.

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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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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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Ramírez-Arroyo, Alejandro, Juan Carlos González-Macías, Jose J. Rico-Palomo, Javier Carmona-Murillo, and Antonio Martínez-González. "On the Spectral Efficiency for Distributed Massive MIMO Systems." Applied Sciences 11, no. 22 (November 18, 2021): 10926. http://dx.doi.org/10.3390/app112210926.

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Distributed MIMO (D-MIMO) systems are expected to play a key role in deployments for future mobile communications. Together with massive MIMO technology, D-MIMO aims to maximize the spectral efficiency and data rate in mobile networks. This paper proposes a deep study on the spectral efficiency of D-MIMO systems for essential channel parameters, such as the channel power balance or the correlation between propagation channels. For that purpose, several propagation channels were acquired in both anechoic and reverberation chambers and were emulated using channel simulators. In addition, several frequency bands were studied, both the sub–6 GHz band and mmWave band. The results of this study revealed the high influence of channel correlation and power balance on the physical channel performance. Low-correlated and high-power balance propagation channels show better performances than high correlated and power unbalance channels in terms of spectral efficiency. Given these results, it will be fundamental to take into account the spectral efficiency of D-MIMO systems when designing criteria to establish multi-connectivity in future mobile network deployments.

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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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Zaki, Amira I., Mahmoud Nassar, Moustafa H. Aly, and Waleed K. Badawi. "A Generalized Spatial Modulation System Using Massive MIMO Space Time Coding Antenna Grouping." Entropy 22, no. 12 (November 30, 2020): 1350. http://dx.doi.org/10.3390/e22121350.

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Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.

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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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Wang, Qianya, and Hongwen Yang. "A Switched Diversity Scheme for Massive MIMO Systems." International Journal of Antennas and Propagation 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/627275.

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With the constraint of antenna space, spatial correlation and mutual coupling must be considered to accurately predict the system performance for massive MIMO systems. Increasing the antenna quantity can degrade the system performance due to mutual coupling. Antenna selection systems have better performance and lower hardware cost than full-MIMO systems. However, the conventional selection combining (SC) scheme consumes a great amount of training overhead and has high operational complexity in the presence of mutual coupling. This paper proposes a group switch-and-examine combining (GSEC) scheme for massive MIMO systems with the spatial correlation and mutual coupling existing at both the transmitter and receiver. Simulation results demonstrate that the proposed GSEC scheme provides better effective capacity performance and lower operational complexity than the conventional selection combining (SC) and full-MIMO scheme.

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Su, Xin, Jie Zeng, Jingyu Li, Liping Rong, Lili Liu, Xibin Xu, and Jing Wang. "Limited Feedback Precoding for Massive MIMO." International Journal of Antennas and Propagation 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/416352.

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The large-scale array antenna system with numerous low-power antennas deployed at the base station, also known as massive multiple-input multiple-output (MIMO), can provide a plethora of advantages over the classical array antenna system. Precoding is important to exploit massive MIMO performance, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a new avenue of codebook design based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular antenna array, which is preferable for the antenna deployment of massive MIMO. Although the feedback overhead is quite limited, the codebook design can provide an effective solution to support multiple users in different scenarios. Simulation results demonstrate that our proposed codebook outperforms the previously known codebooks remarkably.

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Kulkarni, Sandeepkumar, and Dr Raju Yanamshetti Kulkarni. "Surveying on MIMO Technology for Future Wireless Communication." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (November 30, 2021): 78–83. http://dx.doi.org/10.35940/ijrte.d6525.1110421.

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Massive MIMO is an extension of traditional MIMO with the exception that the BSs in massive MIMO are equipped with large number of antennas, usually hundred or more. This large number of antennas provide several positive advantages towards wireless communication with respect to increasing volume of data traffic. Each antenna is capable of serving multiple users simultaneously leading to reduction in power consumption as well as data rate amplification. Additionally, narrow and more focused beams are pointed to individual user devices located at the cell edge thereby upgrading of downlink signal quality. Using massive MIMO technique also increases reliability of the links, reduces noise effects, and mitigates and interference. With increasing number of users gets service, the throughput of the system also increases.

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Su, Xin, and KyungHi Chang. "Diversity and Multiplexing Technologies by 3D Beams in Polarized Massive MIMO Systems." Mobile Information Systems 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/2318287.

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Massive multiple input, multiple output (M-MIMO) technologies have been proposed to scale up data rates reaching gigabits per second in the forthcoming 5G mobile communications systems. However, one of crucial constraints is a dimension in space to implement the M-MIMO. To cope with the space constraint and to utilize more flexibility in 3D beamforming (3D-BF), we propose antenna polarization in M-MIMO systems. In this paper, we design a polarized M-MIMO (PM-MIMO) system associated with 3D-BF applications, where the system architectures for diversity and multiplexing technologies achieved by polarized 3D beams are provided. Different from the conventional 3D-BF achieved by planar M-MIMO technology to control the downtilted beam in a vertical domain, the proposed PM-MIMO realizes 3D-BF via the linear combination of polarized beams. In addition, an effective array selection scheme is proposed to optimize the beam-width and to enhance system performance by the exploration of diversity and multiplexing gains; and a blind channel estimation (BCE) approach is also proposed to avoid pilot contamination in PM-MIMO. Based on the Long Term Evolution-Advanced (LTE-A) specification, the simulation results finally confirm the validity of our proposals.

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41

Matalatala, Michel, Margot Deruyck, Emmeric Tanghe, Luc Martens, and Wout Joseph. "Optimal Low-Power Design of a Multicell Multiuser Massive MIMO System at 3.7 GHz for 5G Wireless Networks." Wireless Communications and Mobile Computing 2018 (October 23, 2018): 1–17. http://dx.doi.org/10.1155/2018/9796784.

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Massive MIMO techniques are expected to deliver significant performance gains for the future wireless communication networks by improving the spectral and the energy efficiencies. In this paper, we propose a method to optimize the positions, the coverage, and the energy consumption of the massive MIMO base stations within a suburban area in Ghent, Belgium, while meeting the low power requirements. The results reveal that massive MIMO provides better performances for the crowded scenario where users’ mobility is limited. With 256 antennas, a massive MIMO base station can simultaneously multiplex 18 users at the same time-frequency resource while consuming 8 times less power and providing 200 times more capacity than a 4G reference network for the same coverage. Moreover, a pilot reuse pattern of 3 is recommended in a multiuser multicell environment to obtain a good tradeoff between the high spectral efficiency and the low power requirement.

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42

Han, Yonggue, Dongkyu Sim, and Chungyong Lee. "Optimization of the Number of Antennas for Energy Efficiency in Massive MIMO WPCN." Journal of the Institute of Electronics and Information Engineers 52, no. 3 (March 25, 2015): 19–24. http://dx.doi.org/10.5573/ieie.2015.52.3.019.

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43

Gupta, Keerti, and Neetu Sikarwar. "A Survey on VLC Based Massive MIMO-OFDM for 5G Networks." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 363–66. http://dx.doi.org/10.31142/ijtsrd21368.

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Jeong, Moo-Woong, and Tae-Won Ban. "Energy Efficient Transmit Antenna Selection Scheme in Multi-User Massive MIMO Networks." Journal of the Korea Institute of Information and Communication Engineering 20, no. 7 (July 31, 2016): 1249–54. http://dx.doi.org/10.6109/jkiice.2016.20.7.1249.

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45

Chen, Xiaomin, Taotao Zhao, Qiang Sun, Qiaosheng Hu, and Miaomiao Xu. "Cell-Free Massive MIMO with Energy-Efficient Downlink Operation in Industrial IoT." Mathematics 10, no. 10 (May 14, 2022): 1687. http://dx.doi.org/10.3390/math10101687.

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Cell-free massive Multi-input Multi-output (MIMO) can offer higher spectral efficiency compared with cellular massive MIMO by providing services to users through the collaboration of distributed APs, and cell-free massive MIMO systems with distributed operations are attracting a great deal of industry attention due to their simplicity and ease of deployment. This paper aims to find an optimal solution for energy efficiency in the downlink operation in the Industrial Internet based on cell-free massive MIMO systems with distributed operations. A system model is proposed, and a theoretical analysis on energy efficiency is presented. The optimization problem of efficient downlink operation is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is further decomposed into two sub-problems, i.e., maximizing the sum-rate of the downlink transmission and optimizing the total energy consumption. The two sub-problems are addressed via AP selection and power allocation, respectively. The simulation results demonstrate that our algorithms can significantly improve the energy efficiency with low computational complexity in comparison with traditional distributed cell-free massive MIMO. Even in the presence of pilot contamination, the proposed algorithms can still provide significant energy efficiency when a large number of IoTDs are connected.

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Abdul Haleem, Mohamed. "On the Capacity and Transmission Techniques of Massive MIMO Systems." Wireless Communications and Mobile Computing 2018 (July 17, 2018): 1–9. http://dx.doi.org/10.1155/2018/9363515.

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A massive MIMO wireless system is a multiuser MISO system where base stations consist of a large number of antennas with respect to number of user devices, each equipped with a single antenna. Massive MIMO is seen as the way forward in enhancing the transmission rate and user capacity in 5G wireless. The potential of massive MIMO system lies in the ability to almost always realize multiuser channels with near zero mutual coupling. Coupling factor reduces by 1/2 for each doubling of transmit antennas. In a high bit rate massive MIMO system with m base station antennas and n users, downlink capacity increases as log2⁡m bps/Hz, and the capacity per user reduces as log2⁡n bps/Hz. This capacity can be achieved by power sharing and using signal weighting vectors aligned to respective 1×m channels of the users. For low bit rate transmission, time sharing achieves the capacity as much as power sharing does. System capacity reduces as channel coupling factor increases. Interference avoidance or minimization strategies can be used to achieve the available capacity in such scenarios. Probability distribution of channel coupling factor is a convenient tool to predict the number of antennas needed to qualify a system as massive MIMO.

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Han, Tongzhou, and Danfeng Zhao. "The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels." Entropy 23, no. 11 (November 22, 2021): 1552. http://dx.doi.org/10.3390/e23111552.

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In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

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Tran, Thanh-Nam, and Miroslav Voznak. "Switchable Coupled Relays Aid Massive Non-Orthogonal Multiple Access Networks with Transmit Antenna Selection and Energy Harvesting." Sensors 21, no. 4 (February 5, 2021): 1101. http://dx.doi.org/10.3390/s21041101.

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The article proposes a new switchable coupled relay model for massive MIMO-NOMA networks. The model equips a much greater number of antennas on the coupled relays to dramatically improve capacity and Energy Efficiency (EE). Each relay in a coupled relay is selected and delivered into a single transmission block to serve multiple devices. This paper also plots a new diagram of two transmission blocks which illustrates energy harvesting and signal processing. To optimize the system performance of a massive MIMO-NOMA network, i.e., Outage Probability (OP) and system throughput, this paper deploys a Transmit Antenna Selection (TAS) protocol to select the best received signals from the pre-coding channel matrices. In addition, to achieve better EE, Simultaneously Wireless Information Power Transmit (SWIPT) is implemented. Specifically, this paper derives the novel theoretical analysis in closed-form expressions, i.e., OP, system throughput and EE from a massive MIMO-NOMA network aided by switchable coupled relays. The theoretical results obtained from the closed-form expressions show that a massive MIMO-NOMA network achieves better OP and greater capacity and expends less energy than the MIMO technique. Finally, independent Monte Carlo simulations verified the theoretical results.

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Li, Xingwang, Lihua Li, Ling Xie, Xin Su, and Ping Zhang. "Performance Analysis of 3D Massive MIMO Cellular Systems with Collaborative Base Station." International Journal of Antennas and Propagation 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/614061.

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Massive MIMO have drawn considerable attention as they enable significant capacity and coverage improvement in wireless cellular network. However, pilot contamination is a great challenge in massive MIMO systems. Under this circumstance, cooperation and three-dimensional (3D) MIMO are emerging technologies to eliminate the pilot contamination and to enhance the performance relative to the traditional interference-limited implementations. Motivated by this, we investigate the achievable sum rate performance of MIMO systems in the uplink employing cooperative base station (BS) and 3D MIMO systems. In our model, we consider the effects of both large-scale and small-scale fading, as well as the spatial correlation and indoor-to-outdoor high-rise propagation environment. In particular, we investigate the cooperative communication model based on 3D MIMO and propose a closed-form lower bound on the sum rate. Utilizing this bound, we pursue a “large-system” analysis and provide the asymptotic expression when the number of antennas at the BS grows large, and when the numbers of antennas at transceiver grow large with a fixed ratio. We demonstrate that the lower bound is very tight and becomes exact in the massive MIMO system limits. Finally, under the sum rate maximization condition, we derive the optimal number of UTs to be served.

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Oh, Ji-Hye, Beom-Sik Shin, Min-A. Kim, Young-Hwan You, Duck-Dong Hwang, and Hyoung-Kyu Song. "Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System." Sensors 22, no. 10 (May 17, 2022): 3794. http://dx.doi.org/10.3390/s22103794.

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A cell-free massive multiple input multiple output (MIMO) system is an attractive network model that is in the spotlight in 5G and future communication systems. Despite numerous advantages, the cell-free massive MIMO system has a problem in that it is difficult to operate in reality due to its vast amount of calculation. The user-centric cell-free massive MIMO model has a more feasible and scalable benefit than the cell-free massive MIMO model. However, this model has the disadvantage that as the number of users in the area increases, there are users who do not receive the service. In this paper, the proposed scheme creates connections for unserved users under a user-centric scheme without additional access point (AP) installation and disconnection for existing users. A downlink user-centric cell-free massive MIMO system model in which the APs are connected to the central processing unit (CPU) and the APs and users are geographically distributed is considered. First, the downlink spectral efficiency formula is derived and applied to the user-centric cell-free massive MIMO system. Then, the proposed scheme and power control algorithm are applied to the derived formula. The simulation results show that the unserved users within the area disappear by using the proposed scheme, while the bit error rate (BER) performance and sum rate improve compared to the existing scheme. In addition, it is shown that the proposed scheme works well even with a very large number of users in the area, and a significant service performance improvement for the worst 10% of users and the overall improvement of per-user throughput for the bottom 70% of users are ensured.

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