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Journal articles on the topic 'Distributed massive MIMO'

Author: Grafiati

Published: 25 May 2024

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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

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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3

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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4

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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5

Van, Son Dinh, Hien Quoc Ngo, and Simon L. Cotton. "Wireless Powered Wearables Using Distributed Massive MIMO." IEEE Transactions on Communications 68, no. 4 (April 2020): 2156–72. http://dx.doi.org/10.1109/tcomm.2020.2965442.

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6

Yuan, Jide, Qi He, Michail Matthaiou, Tony Q. S. Quek, and Shi Jin. "Toward Massive Connectivity for IoT in Mixed-ADC Distributed Massive MIMO." IEEE Internet of Things Journal 7, no. 3 (March 2020): 1841–56. http://dx.doi.org/10.1109/jiot.2019.2957281.

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7

Ding, Guoru, Xiqi Gao, Zhen Xue, Yongpeng Wu, and Qingjiang Shi. "Massive MIMO for Distributed Detection With Transceiver Impairments." IEEE Transactions on Vehicular Technology 67, no. 1 (January 2018): 604–17. http://dx.doi.org/10.1109/tvt.2017.2747772.

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8

Pradhan, Devasis, A. Dash, Hla Myo Tun, Naw Khu Say Wah, and Thandar Oo. "Improvement of Capacity and QoE: Distributed Massive MIMO (DM-MIMO) Technology-5G." Journal of Network Security Computer Networks 8, no. 3 (September 28, 2022): 9–17. http://dx.doi.org/10.46610/jonscn.2022.v08i03.002.

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Distributed massive MIMO is an arising answer for improve 5G limit, throughput, and QoE inside. There are various ways to advance indoor execution. The indoor cell network should have totally different qualities and needs from its outside partner, yet a consistent encounter should be kept up with while moving between the two. New arrangements, like small cells and distributed antenna systems (DAS), have been added to every portable age to work on the presentation and quality of experience (QoE) for indoor clients. Notwithstanding, the goal lines are moved with each new age of purpose cases, and, surprisingly, more significant levels of execution and QoE are required. The improvement in the indoor experience needed in the 5G period should be extensive. The 5G guidelines empower the organizations to help tremendous quantities of clients or associated gadgets in little spaces (up to 1 million for each square kilometer), while consuming information at multi-gigabit speeds and with low idleness and high unwavering quality. This paper give a brief review on improvement in channel capacity and QoE using massiveness of MIMO technology for 5G environment in distributed manner.

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9

Zhu, Yi-Hang, Gilles Callebaut, Hatice Çalık, Liesbet Van der Perre, and François Rottenberg. "Energy Efficient Access Point Placement for Distributed Massive MIMO." Network 2, no. 2 (May 11, 2022): 288–310. http://dx.doi.org/10.3390/network2020019.

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Distributed massive multiple-input multiple-output (D-mMIMO) is one of the key candidate technologies for future wireless networks. A D-mMIMO system has multiple, geographically distributed, access points (APs) jointly serving its users. First of all, this paper reports on where to position these APs to minimize the overall transmit power in actual deployments. As a second contribution, we show that it is essential to take into account both the radiation pattern of the antenna array and the environment information when optimizing AP placement. Neglecting the radiation pattern and environment information, as generally assumed in the literature, can lead to a power penalty in the order of 15 dB and 20 dB, respectively. These results have been obtained by formulating the AP placement optimization problem as a combinatorial optimization problem, which can be solved with different approaches where different channel models are applied. The proposed graph-based channel model drastically lowers the computational time with respect to using an ray-tracing simulator (RTS) for channel evaluation. The performance of the graph-based approach is validated via the RTS, showing that it achieves 5 dB power saving on average compared with a Euclidean distance-based approach, which is the most commonly used approach in the literature.

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10

Albreem, Mahmoud A., Alaa Alhabbash, Ammar M. Abu-Hudrouss, and Tarik Adnan Almohamad. "Data detection in decentralized and distributed massive MIMO networks." Computer Communications 189 (May 2022): 79–99. http://dx.doi.org/10.1016/j.comcom.2022.03.015.

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11

Yan, Hangsong, and I.-Tai Lu. "Asynchronous Reception Effects on Distributed Massive MIMO-OFDM System." IEEE Transactions on Communications 67, no. 7 (July 2019): 4782–94. http://dx.doi.org/10.1109/tcomm.2019.2908401.

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12

Akbarpour-Kasgari, Abbas, and Mehrdad Ardebilipour. "Massive MIMO-OFDM Channel Estimation via Distributed Compressed Sensing." IEEE Wireless Communications Letters 8, no. 2 (April 2019): 376–79. http://dx.doi.org/10.1109/lwc.2018.2873339.

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13

Yuan, Shitong, and Qilian Liang. "3D nested distributed massive MIMO: Modeling and performance analysis." Ad Hoc Networks 58 (April 2017): 6–12. http://dx.doi.org/10.1016/j.adhoc.2016.12.007.

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14

Meyer, E., D. Kruglov, M. Krivic, M. Tanveer, R. Argaez-Ramirez, Y. Zhang, A. Briseno Ojeda, et al. "The state of the art in beyond 5G distributed massive multiple-input multiple-output communication system solutions." Open Research Europe 2 (September 2, 2022): 106. http://dx.doi.org/10.12688/openreseurope.14501.1.

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Beyond fifth generation (5G) communication systems aim towards data rates in the tera bits per second range, with improved and flexible coverage options, introducing many new technological challenges in the fields of network architecture, signal processing, and radio frequency front-ends. One option is to move towards cell-free, or distributed massive Multiple-Input Multiple-Output (MIMO) network architectures and highly integrated front-end solutions. This paper presents an outlook on beyond 5G distributed massive MIMO communication systems, the signal processing, characterisation and simulation challenges, and an overview of the state of the art in millimetre wave antennas and electronics.

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15

Lv, Qian, Jiamin Li, Pengcheng Zhu, Dongming Wang, and Xiaohu You. "Downlink Spectral Efficiency Analysis in Distributed Massive MIMO with Phase Noise." Electronics 7, no. 11 (November 12, 2018): 317. http://dx.doi.org/10.3390/electronics7110317.

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To achieve the advantages provided by massive multiple-input multiple-output (MIMO), a large number of antennas need to be deployed at the base station. However, for the reason of cost, inexpensive hardwares are employed in the realistic scenario, which makes the system distorted by hardware impairments. Hence, in this paper, we analyze the downlink spectral efficiency in distributed massive MIMO with phase noise and amplified thermal noise. We provide an effective channel model considering large-scale fading, small-scale fast fading and phase noise. Based on the model, the estimated channel state information (CSI) is obtained during the pilot phase. Under the imperfect CSI, the closed-form expressions of downlink achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) precoders in distributed massive MIMO are derived. Furthermore, we also give the user ultimate achievable rates when the number of antennas tends to infinity with both precoders. Based on these expressions, we analyze the impacts of phase noise on the spectral efficiency. It can be concluded that the same limit rate is achieved with both precoders when phase noise is present, and phase noise limits the spectral efficiency. Numerical results show that ZF outdoes MRT precoder in spectral efficiency and ZF precoder is more affected by phase noise.

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Xiong, Sian, Zhipeng Chen, Nan Jiang, Jiahui Zhao, and Lingfeng Liu. "Performance Optimization of Multipair Massive MIMO Polarized Relay Systems." Electronics 12, no. 14 (July 22, 2023): 3184. http://dx.doi.org/10.3390/electronics12143184.

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In this paper, we analyze the challenges faced by multipair massive Multiple-input-multiple-output (MIMO) relay channels in 5G wireless communication systems, where high path loss and severe shadow fading between different user nodes can cause poor channel quality. To overcome these limitations, we propose a polarization selection scheme for antenna arrays combining multipair massive MIMO relay and beamforming to improve MIMO relay channel quality. Specifically, our main goal is to exploit the potential of polarization diversity to maintain and improve the link quality while maintaining the compact size of the MIMO antenna array. To achieve this goal, we introduce a dynamic weighted particle swarm optimization algorithm with contraction factor (CF-DWPSO) to select the polarization direction. In addition, we employ distributed beamforming to effectively suppress or eliminate inter-pair interference. The performance of the simulation analysis shows that CF-DWPSO combined with beamforming provides significant performance improvement of the multipair massive MIMO polarized relay channels, which further indicates that it is of great necessity for improving the performance of this system to optimize the polarization selection by combining beamforming techniques.

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17

Ding, Jie, Daiming Qu, Pei Liu, and Jinho Choi. "Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO." IEEE Transactions on Communications 69, no. 4 (April 2021): 2317–30. http://dx.doi.org/10.1109/tcomm.2021.3051202.

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18

Senanayake, Rajitha, Peter Smith, Phee Lep Yeoh, and Jamie Evans. "An SNR Approximation for Distributed Massive MIMO With Zero Forcing." IEEE Communications Letters 19, no. 11 (November 2015): 1885–88. http://dx.doi.org/10.1109/lcomm.2015.2474377.

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19

Minasian, Arin, Raviraj S. Adve, Shahram Shahbazpanahi, and Gary Boudreau. "On RRH Placement for Multi-User Distributed Massive MIMO Systems." IEEE Access 6 (2018): 70597–614. http://dx.doi.org/10.1109/access.2018.2880149.

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20

Kuo, Wen-Hsing, and Chia-How Chen. "Distributed Antenna Allocation Scheme for Massive MIMO Cellular Backhaul Networks." IEEE Access 6 (2018): 73895–904. http://dx.doi.org/10.1109/access.2018.2884520.

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21

Siljak, Harun, Kyriaki Psara, and Anna Philippou. "Distributed Antenna Selection for Massive MIMO Using Reversing Petri Nets." IEEE Wireless Communications Letters 8, no. 5 (October 2019): 1427–30. http://dx.doi.org/10.1109/lwc.2019.2920128.

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22

Kim, W., and W. Yoon. "Energy efficiency maximisation for WPCN with distributed massive MIMO system." Electronics Letters 52, no. 19 (September 2016): 1642–44. http://dx.doi.org/10.1049/el.2016.2167.

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23

Al Ayidh, Abdulrahman, Yusuf Sambo, Sofiat Olaosebikan, Shuja Ansari, and Muhammad Ali Imran. "Antenna Selection Based on Matching Theory for Uplink Cell-Free Millimetre Wave Massive Multiple Input Multiple Output Systems." Telecom 3, no. 3 (July 7, 2022): 448–66. http://dx.doi.org/10.3390/telecom3030024.

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In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.

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24

Yu, Seoyoung, and Jeong Woo Lee. "Channel Sounding for Multi-User Massive MIMO in Distributed Antenna System Environment." Electronics 8, no. 1 (January 1, 2019): 36. http://dx.doi.org/10.3390/electronics8010036.

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We propose a generation scheme for a sounding reference signal (SRS) suitable for supporting a large number of users in massive multi-input multi-output (MIMO) system with a distributed antenna system (DAS) environment. The proposed SRS can alleviate the pilot contamination problem which occurs inherently in the multi-user system due to the limited number of orthogonal sequences. The proposed SRS sequence is generated by applying a well-chosen phase rotation to the conventional LTE/LTE-A SRS sequences without requiring an increased amount of resource usage. We also propose using the correlation-aided channel estimation algorithm as a supplemental scheme to obtain more reliable and refined channel estimation. It is shown that the proposed SRS sequence and the supplemental channel estimation scheme improve significantly the channel estimation performance in multi-user massive MIMO systems.

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25

Pérez, Jesús R., Óscar Fernández, Luis Valle, Abla Bedoui, Mohamed Et-tolba, and Rafael P. Torres. "Experimental Analysis of Concentrated versus Distributed Massive MIMO in an Indoor Cell at 3.5 GHz." Electronics 10, no. 14 (July 10, 2021): 1646. http://dx.doi.org/10.3390/electronics10141646.

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This paper presents a measurement-based comparison between distributed and concentrated massive multiple-input multiple-output (MIMO) systems, which are called D-mMIMO and C-mMIMO systems, in an indoor environment considering a 400 MHz bandwidth centered at 3.5 GHz. In both cases, we have considered an array of 64 antennas in the base station and eight simultaneously active users. The work focuses on the characterization of both schemes in the up-link, considering the analysis of the sum capacity, the total spectral efficiency (SE) achievable by using the zero forcing (ZF) combining method, as well as the user fairness. The effect of the power imbalance between the different transmitters or user terminal (UT) locations, and thus, the benefits of performing an adequate power control are also investigated. The differences between the C-mMIMO and D-mMIMO channel performances are explained through the observation of the structure of their respective measured channel matrices through parameters such as the condition number or the power imbalance between the channels established by each UT. The channel measurements have been performed in the frequency domain, emulating a massive MIMO system in the framework of a time-domain duplex orthogonal frequency multiple access network (TDD-OFDM-MIMO). The characterization of the MIMO channel is based on the virtual array technique for both C-mMIMO and D-mMIMO systems. The deployment of the C-mMIMO and D-MIMO systems, as well as the distribution of users in the measurement environment, has been arranged as realistically as possible, avoiding the movement of people or machines.

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26

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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Kassam, Joumana, Daniel Castanheira, Adão Silva, Rui Dinis, and Atílio Gameiro. "A Review on Cell-Free Massive MIMO Systems." Electronics 12, no. 4 (February 17, 2023): 1001. http://dx.doi.org/10.3390/electronics12041001.

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Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks.

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González-Aurioles, Santiago, J. L. Padilla, P. Padilla, Juan F. Valenzuela-Valdés, and Juan C. Gonzalez-Macias. "On the MIMO Capacity for Distributed System under Composite Rayleigh/Rician Fading and Shadowing." International Journal of Antennas and Propagation 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/105017.

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Wireless channels are commonly affected by short-term fading and long-term fading (shadowing). The shadowing effects must be taken into account also when mobility is present in the wireless scenario. Using a composite fading model, the total channel capacity can be studied for a scenario with short-term Rayleigh fading along with shadowing. This work provides quantitative results for these kinds of scenarios with Rayleigh fading and shadowing, considering also multiple-input and multiple-output systems, which have not been previously reported. In addition, the channel capacity has been studied in depth in its relation with the shadowing level, signal to noise ratio, and the number of elements in the multiple-input and multiple-output system. Moreover, the channel performance with shadowing has been compared to the one without it. Furthermore, Rician model with shadowing is studied and its results are reported. In addition, correlated and experimental results are provided. It is identified that the distributed MIMO systems can benefit from shadowing in Rician channels. This advantage has not been reported previously. This type of fading is proposed for massive MIMO by others and our results open the door to emulate massive MIMO on a reverberation chamber.

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29

Wei, Guofeng, Bangning Zhang, Guoru Ding, Bing Zhao, Yimin Wei, and Daoxing Guo. "Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks." Sensors 20, no. 7 (April 3, 2020): 2005. http://dx.doi.org/10.3390/s20072005.

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For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.

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Li, Jiamin, Qian Lv, Jing Yang, Pengcheng Zhu, and Xiaohu You. "Spectral and Energy Efficiency of Distributed Massive MIMO with Low-Resolution ADC." Electronics 7, no. 12 (December 4, 2018): 391. http://dx.doi.org/10.3390/electronics7120391.

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In this paper, considering a more realistic case where the low-resolution analog-to-digital convertors (ADCs) are employed at receiver antennas, we investigate the spectral and energy efficiency in multi-cell multi-user distributed massive multi-input multi-output (MIMO) systems with two linear receivers. An additive quantization noise model is provided first to study the effects of quantization noise. Using the model provided, the closed-form expressions for the uplink achievable rates with a zero-forcing (ZF) receiver and a maximum ratio combination (MRC) receiver under quantization noise and pilot contamination are derived. Furthermore, the asymptotic achievable rates are also given when the number of quantization bits, the per user transmit power, and the number of antennas per remote antenna unit (RAU) go to infinity, respectively. Numerical results prove that the theoretical analysis is accurate and show that quantization noise degrades the performance in spectral efficiency, but the growth in the number of antennas can compensate for the degradation. Furthermore, low-resolution ADCs with 3 or 4 bits outperform perfect ADCs in energy efficiency. Numerical results imply that it is preferable to use low-resolution ADCs in distributed massive MIMO systems.

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31

Wang, Dongming, Chuan Zhang, Yongqiang Du, Jianing Zhao, Ming Jiang, and Xiaohu You. "Implementation of a Cloud-Based Cell-Free Distributed Massive MIMO System." IEEE Communications Magazine 58, no. 8 (August 2020): 61–67. http://dx.doi.org/10.1109/mcom.001.2000106.

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32

Liu, Dantong, Lifeng Wang, Yue Chen, Tiankui Zhang, Kok Keong Chai, and Maged Elkashlan. "Distributed Energy Efficient Fair User Association in Massive MIMO Enabled HetNets." IEEE Communications Letters 19, no. 10 (October 2015): 1770–73. http://dx.doi.org/10.1109/lcomm.2015.2454504.

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33

Xu, Jun, Pengcheng Zhu, Jiamin Li, Xiaodong Wang, and Xiaohu You. "Secrecy Energy Efficiency Optimization for Multi-User Distributed Massive MIMO Systems." IEEE Transactions on Communications 68, no. 2 (February 2020): 915–29. http://dx.doi.org/10.1109/tcomm.2019.2955488.

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34

Guo, Kaifeng, Yan Guo, and Gerd Ascheid. "Security-Constrained Power Allocation in MU-Massive-MIMO With Distributed Antennas." IEEE Transactions on Wireless Communications 15, no. 12 (December 2016): 8139–53. http://dx.doi.org/10.1109/twc.2016.2612636.

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35

Biswas, Sudip, Jiang Xue, Faheem A. Khan, and Tharmalingam Ratnarajah. "Performance Analysis of Correlated Massive MIMO Systems With Spatially Distributed Users." IEEE Systems Journal 12, no. 2 (June 2018): 1850–61. http://dx.doi.org/10.1109/jsyst.2016.2594155.

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36

Zhang, Xianyu, Daoxing Guo, Kang An, Wenfeng Ma, and Kefeng Guo. "Secure transmission and power allocation in multiuser distributed massive MIMO systems." Wireless Networks 26, no. 2 (September 25, 2018): 941–54. http://dx.doi.org/10.1007/s11276-018-1840-y.

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37

Zhang, Wenjie, Hui Li, Rong Jin, Shanlin Wei, Wei Cheng, Weisi Kong, and Penglu Liu. "Distributed Structured Compressive Sensing-Based Time-Frequency Joint Channel Estimation for Massive MIMO-OFDM Systems." Mobile Information Systems 2019 (May 2, 2019): 1–16. http://dx.doi.org/10.1155/2019/2634361.

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In massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, accurate channel state information (CSI) is essential to realize system performance gains such as high spectrum and energy efficiency. However, high-dimensional CSI acquisition requires prohibitively high pilot overhead, which leads to a significant reduction in spectrum efficiency and energy efficiency. In this paper, we propose a more efficient time-frequency joint channel estimation scheme for massive MIMO-OFDM systems to resolve those problems. First, partial channel common support (PCCS) is obtained by using time-domain training. Second, utilizing the spatiotemporal common sparse property of the MIMO channels and the obtained PCCS information, we propose the priori-information aided distributed structured sparsity adaptive matching pursuit (PA-DS-SAMP) algorithm to achieve accurate channel estimation in frequency domain. Third, through performance analysis of the proposed algorithm, two signal power reference thresholds are given, which can ensure that the signal can be recovered accurately under power-limited noise and accurately recovered according to probability under Gaussian noise. Finally, pilot design, computational complexity, spectrum efficiency, and energy efficiency are discussed as well. Simulation results show that the proposed method achieves higher channel estimation accuracy while requiring lower pilot sequence overhead compared with other methods.

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38

Hoffmann, Marcin, and Paweł Kryszkiewicz. "Evaluation of User-Centric Cell-Free Massive Multiple-Input Multiple-Output Networks Considering Realistic Channels and Frontend Nonlinear Distortion." Applied Sciences 14, no. 5 (February 20, 2024): 1684. http://dx.doi.org/10.3390/app14051684.

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Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.

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39

Salehi, Pouria, Naser Parhizgar, and Farshad Pesaran. "A New Cooperative and Distributed Antenna Structure in Massive MIMO-NOMA Based on mmWave Transmission Scheme." Wireless Communications and Mobile Computing 2021 (October 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/6717077.

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In this paper, we propose a new massive multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) system with a cooperative and distributed antenna structure based on a millimeter-wave (mmWave) transmission system. We proposed this method to obtain high energy efficiency (EE) and spectrum efficiency (SE) by using the mmWave transmission scheme. In the proposed system, the user selects a few nearby base stations (BS) to create a virtual cell to own the serving BS antenna set. We concentrate on the mmWave massive MIMO-NOMA scheme. In this scheme, a large number of BS antennas and users by uniform distributions (UD) are considered in a specific area. Also, we combine our proposed method by interleaving division multiple access (IDMA) and utilize the IMDA benefits for high-rate applications. The proposed transmission scheme significantly improves the performance output in terms of SE, EE, sum rate, and log sum rate, according to our simulation results.

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Iwabuchi, Masashi, Yoghitha Ramamoorthi, and Kei Sakaguchi. "User-Driven Relay Beamforming for mmWave Massive Analog-Relay MIMO." Sensors 23, no. 2 (January 16, 2023): 1034. http://dx.doi.org/10.3390/s23021034.

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Sixth-generation mobile communication (6G) aims to further improve capacity and reliability by controlling the radio propagation environment. Millimeter wave (mmWave) high-frequency band communication offers large bandwidth at the cost of high attenuation, even for smaller distances. Due to this, fewer multiple input multiple outputs (MIMO) multiplexing is possible at the base station (BS). Distributed analog relay nodes with beamforming capability improve the received power and MIMO multiplexing of mmWave communication. Due to limited signal processing, the analog relay node cannot perform beam search and tracking using these mmWave reference signals. The beam search and tracking are possible at BS or user equipment at the cost of increased control overhead. To reduce this overhead and provide relay-based 6G communication, we propose user-driven relay beamforming methods which can obtain the benefits of a massive analog relay MIMO. Assuming vehicular-to-everything (V2X) as a 6G application, we considered a relay-beam control method that uses the user information (location, velocity, acceleration, and direction of the terminal) contained in intelligent transport systems (ITS) messages called Cooperative Awareness Message (CAM). Simulation results show that the proposed method significantly reduces the overhead and the obtains benefits of the massive analog-relay MIMO. Furthermore, the accuracy of CAM’s location information, the control period, and the effects of UE mobility are evaluated and presented. The results also show that the proposed method can work effectively in future V2X applications.

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41

Fitzgerald, Emma, and Michał Pióro. "Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO." Future Internet 16, no. 3 (February 23, 2024): 71. http://dx.doi.org/10.3390/fi16030071.

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Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios.

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42

Ye, Feng, Jiamin Li, Pengcheng Zhu, Dongming Wang, and Xiaohu You. "Fingerprint-Based Covariance Matrix Estimation for Cell-Free Distributed Massive MIMO Systems." IEEE Wireless Communications Letters 11, no. 2 (February 2022): 416–20. http://dx.doi.org/10.1109/lwc.2021.3130942.

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43

Chawla, Apoorva, Rakesh Kumar Singh, Adarsh Patel, Aditya K. Jagannatham, and Lajos Hanzo. "Distributed Detection for Centralized and Decentralized Millimeter Wave Massive MIMO Sensor Networks." IEEE Transactions on Vehicular Technology 70, no. 8 (August 2021): 7665–80. http://dx.doi.org/10.1109/tvt.2021.3089669.

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Prasad, K. N. R. Surya Vara, Ekram Hossain, and Vijay K. Bhargava. "Machine Learning Methods for RSS-Based User Positioning in Distributed Massive MIMO." IEEE Transactions on Wireless Communications 17, no. 12 (December 2018): 8402–17. http://dx.doi.org/10.1109/twc.2018.2876832.

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Wang, Xinhua, Yan Yang, and Jinlu Sheng. "Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems." Future Internet 9, no. 2 (June 9, 2017): 21. http://dx.doi.org/10.3390/fi9020021.

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46

Kamga, Gervais N., Minghua Xia, and Sonia Aissa. "Spectral-Efficiency Analysis of Massive MIMO Systems in Centralized and Distributed Schemes." IEEE Transactions on Communications 64, no. 5 (May 2016): 1930–41. http://dx.doi.org/10.1109/tcomm.2016.2519513.

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47

Zaib, Alam, Mudassir Masood, Anum Ali, Weiyu Xu, and Tareq Y. Al-Naffouri. "Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems." IEEE Transactions on Communications 64, no. 11 (November 2016): 4607–21. http://dx.doi.org/10.1109/tcomm.2016.2593924.

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48

Minasian, Arin, Shahram Shahbazpanahi, and Raviraj S. Adve. "Distributed Massive MIMO Systems With Non-Reciprocal Channels: Impacts and Robust Beamforming." IEEE Transactions on Communications 66, no. 11 (November 2018): 5261–77. http://dx.doi.org/10.1109/tcomm.2018.2859937.

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49

Xu, Jun, Pengcheng Zhu, Jiamin Li, and Xiaohu You. "Deep Learning-Based Pilot Design for Multi-User Distributed Massive MIMO Systems." IEEE Wireless Communications Letters 8, no. 4 (August 2019): 1016–19. http://dx.doi.org/10.1109/lwc.2019.2904229.

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50

Yue, Dian-Wu, and Ha H. Nguyen. "Multiplexing Gain Analysis of mmWave Massive MIMO Systems With Distributed Antenna Subarrays." IEEE Transactions on Vehicular Technology 68, no. 11 (November 2019): 11368–73. http://dx.doi.org/10.1109/tvt.2019.2943663.

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