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Coverage, Throughput, and Energy Efficiency Enhancement in Beamspace massive MIMO System Using Rate-Splitting and Orthogonal Precoding

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IEEE ACCESS 2024年 12卷 91957-91973页

作者： Alimo, David Hamamura, Masanori Sabuj, Saifur Rahman Kochi Univ Technol Grad Sch Engn Kami Kochi 7828502 Japan Kochi Univ Technol Sch Informat Kami Kochi 7828502 Japan Brac Univ Dept Elect & Elect Engn Dhaka 1212 Bangladesh

Millimeter-wave beamspace massive multiple-input multiple-output system with a lens antenna array can minimize transceiver hardware complexity without compromising performance. However, the number of supported portable user terminals cannot exceed the number of radio frequency blocks accessible at the same time, frequency, and coding resources. In this paper, we propose the integration of rate-splitting multiple access and orthogonal random precoding into the downlink of beamspace massive multiple-input multiple-output system to support a larger number of portable user terminals than the number of available radio frequency blocks while minimizing both inter- and intra-beam interferences and extending the cell coverage percentage. Then, we formulate an optimization problem to optimize the system's overall throughput while keeping the minimum needed throughput and power budget in consideration. The nonconvex optimization issue is then approximated into a convex optimization problem using the successive convex approximation approach. Following that, we offer an alternating method to solve the approximate optimization issue and select an optimal solution. Furthermore, we deduce an analytic expression for the downlink cell coverage percentage and evaluate the effectiveness of the suggested method in terms of total throughput, energy efficiency, and cell coverage percentage. Finally, we compare the proposed method with benchmark techniques for perfect and imperfect channel state information, and numerical results confirm the superior performance of the proposed method over benchmark techniques in terms of sum throughput, energy efficiency, and cell coverage percentage.

关键词： Throughput Downlink Interference Precoding Millimeter wave communication NOMA Wireless networks Energy efficiency MIMO communication Beamspace cell coverage percentage energy efficiency lens antenna array massive multiple-input multiple-output orthogonal random precoding rate-splitting multiple access throughput

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Deep Learning for Detection and Identification of Asynchronous Pilot Spoofing Attacks in massive MIMO Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第11期23卷 17103-17114页

作者： Choudhury, Fuad Ikhlef, Aissa Saad, Walid Debbah, Merouane Univ Durham Dept Engn Durham DH1 3LE England Barclays Investment Bank Markets Post Trade Grp Knutsford WA16 9EU England Virginia Tech Bradley Dept Elect & Comp Engn Arlington VA 22203 USA Khalifa Univ Sci & Technol 6G KU Res Ctr Abu Dhabi U Arab Emirates

massive multiple-input multiple-output (MIMO) networks are highly vulnerable to an active eavesdropping attack called pilot spoofing attack. The pilot spoofing attack causes information leakage to the active eavesdropper (ED) and also weakens the strength of the signal received by the attacked legitimate user equipment (UE) during the downlink transmission. In this paper, a deep neural network, called identification network (IDNet), is proposed to detect asynchronous pilot spoofing attacks and identify the attacked UE. We show that an asynchronous pilot spoofing attack leads to increasing the signal subspace dimension by one unlike the synchronous one. This property is then exploited to improve the attack detection/identification accuracy. In the proposed IDNet, the input features are the eigenvalues of the sample covariance matrix of the received signal at the base station (BS) as well as the ratio between the power of the received signal at the BS projected onto the pilot signals and its expected value. Numerical results show the effectiveness of IDNet in identifying the attacked UE and reveal that the larger the timing and/or frequency mismatches of the ED, the higher the identification accuracy confirming that asynchronous pilot spoofing attacks can be identified more accurately than synchronous pilot spoofing attacks.

关键词： Uplink massive MIMO Channel estimation Antenna arrays Transmitting antennas Synchronization Downlink Deep neural networks massive multiple-input multiple-output physical layer security asynchronous pilot spoofing attack attack detection and identification

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EVM Analysis of Distributed massive MIMO With 1-Bit Radio-Over-Fiber Fronthaul

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第11期72卷 7342-7356页

作者： Hu, Anzhong Aabel, Lise Durisi, Giuseppe Jacobsson, Sven Coldrey, Mikael Fager, Christian Studer, Christoph Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Ericsson AB S-41756 Gothenburg Sweden Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Chalmers Univ Technol Dept Microtechnol & Nanosci S-41296 Gothenburg Sweden Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland

We analyze the uplink performance of a distributed massive multiple-input multiple-output (MIMO) architecture in which the remotely located access points (APs) are connected to a central processing unit via a fiber-optical fronthaul carrying a dithered and 1-bit quantized version of the received radio-frequency (RF) signal. The innovative feature of the proposed architecture is that no down-conversion is performed at the APs. This eliminates the need to equip the APs with local oscillators, which may be difficult to synchronize. Under the assumption that a constraint is imposed on the amount of data that can be exchanged across the fiber-optical fronthaul, we investigate the tradeoff between spatial oversampling, defined in terms of the total number of APs, and temporal oversampling, defined in terms of the oversampling factor selected at the central processing unit, to facilitate the recovery of the transmitted signal from 1-bit samples of the RF received signal. Using the so-called error-vector magnitude (EVM) as performance metric, we shed light on the optimal design of the dither signal, and quantify, for a given number of APs, the minimum fronthaul rate required for our proposed distributed massive MIMO architecture to outperform a standard co-located massive MIMO architecture in terms of EVM.

关键词： massive MIMO RF signals Uplink Quantization (signal) Central Processing Unit Band-pass filters Synchronization Bussgang's theorem nonsubtractive dithering error vector magnitude massive multiple-input multiple-output orthogonal frequency-division multiplexing 1-bit analog-to-digital converters

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Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC

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IEEE ACCESS 2024年 12卷 128702-128716页

作者： Shin, Suho Anokye, Prince Lee, Hoon Moon, Jihwan Lee, Kyoung-Jae Hanbat Natl Univ Dept Elect Engn Daejeon 34158 South Korea IMT Atlantique Lab STICC F-29238 Brest France Ulsan Natl Inst Sci & Technol UNIST Dept Elect Engn Ulsan 44919 South Korea Hanbat Natl Univ Dept Mobile Convergence Engn Daejeon 34158 South Korea

This paper addresses the fair distribution of available energy (AE) in a massive multiple-input multiple-output (mMIMO) simultaneous wireless information and power transfer (SWIPT) sensor network, where sensor nodes are equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We define the system model and estimate the channel using linear minimum mean square error (LMMSE) estimation. To analyze system performance, we derive closed-form expressions for the signal-to-interference-plus-noise ratio (SINR), and harvested energy. These expressions allow us to evaluate the impact of low-resolution ADCs/DACs on both the achievable rate and energy harvesting. Based on these expressions, we formulate an optimization problem fair AE distribution. To solve this non-convex problem, we propose an alternating optimization (AO) algorithm that optimizes the downlink/uplink (DL/UL) powers and the power splitting (PS) ratio. Monte Carlo simulations confirm that our closed-form analysis accurately represents the system performance and that the optimization algorithm distributes AE more fairly compared to the equal power and power splitting case.

关键词： Channel estimation Simultaneous wireless information and power transfer Wireless sensor networks Power demand Optimization Energy resolution Batteries AC-DC power converters DC-AC power converters Power distribution Available energy low-resolution analog-to-digital converters/digital-to-analog converters massive multiple-input multiple-output optimization sensor network

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Implementation of Hybrid Wild Geese Migration-Bird Swarm Algorithm-Based Optimal Power Allocation Strategy for Spectral Efficiency Analysis in massive MIMO System

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第5期36卷

作者： Jallu, Swathi Raju, K. Padma Aditya Inst Technol & Management Dept ECE Tekkali India Jawaharlal Nehru Technol Univ Dept ECE Kakinada India

Spectral Efficiency (SE) plays a crucial role in designing and transmitting the amount of data in wireless communication systems that is an important measure for validating the effectiveness of cellular systems. It determines the usage of a limited frequency spectrum, but it fails to measure consumed power. Numerous methods have been developed to analyze the multiple-input multiple-output (MIMO) system with diverse channel methods. This MIMO system effectively enhances the data throughput, and it can serve diverse users simultaneously within the same frequency band. It significantly enhances the system framework and minimizes the impact of signal fading. However, the MIMO system faces challenges like lower transmit power, maximized coverage space, higher spectral efficiency, and multiplexing gain. To solve these issues, a novel optimization algorithm is developed in massive Multi-User MIMO (MU-MIMO) for maximizing SE in a Downlink (DL) system. This DL communication system is utilized to determine the complex channel conditions among the Base Station (BS) and user equipment. Also, it provides better data transmission control, and it carefully manages the signal strength in the MIMO system. Here, many antennas are provided in BS to distribute individual antenna consumers at the same time in a similar frequency band, and the duplex mode time division uses the beamforming training scheme. An optimal resource allocation together determines the DL transmission signal power training, Uplink (UL) transmission signal power training, UL transmission training duration, and DL transmission to increase the SE, data signal power initiated from the complete given energy budget. Since SE is the main concern of this work, this paper implements an efficient heuristic mechanism for increasing SE in a massive MIMO (M-MIMO) method. The M-MIMO system accurately provides the best cell edge coverage and maximizes network capacity. It achieves better spectral efficiency to enhance overall t

关键词： integrating the position of wild geese and bird swarm algorithm massive multiple-input multiple-output power and resource allocation spectral efficiency uplink and downlink transmission

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DRL-Based AP Selection in Downlink Cell-Free massive MIMO Network With Pilot Contamination

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IEEE COMMUNICATIONS LETTERS 2024年第6期28卷 1432-1436页

作者： Gao, Zhichao Zhang, Qian Liu, Ju Du, Zhengfeng Li, Yunxiao Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

Cell-free massive multiple-input multiple-output (MIMO) network includes numerous geographically distributed access points (APs) serving users through coherent transmission and reception. To achieve scalability, each user should be assigned a personalized cluster of APs. In this letter, we propose a deep reinforcement learning (DRL)-based approach to determine the cluster of APs for each user while satisfying constraints on minimum rates for all users, considering practical concerns such as pilot contamination and statistical channel state information (CSI). Simulation results demonstrate that the proposed DRL-based AP selection scheme outperforms other conventional schemes.

关键词： massive MIMO Downlink Uplink Signal processing algorithms Contamination Training Optimization Cell-free massive multiple-input multiple-output deep reinforcement learning access point selection

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DeepEigen-Tabu: Deep Eigen Network Assisted Probabilistic Tabu Search for massive MIMO Detection

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 13292-13308页

作者： Lu, Hoang-Yang Azizi, S. Pourmohammad Cheng, Shyi-Chyi Natl Taiwan Ocean Univ Dept Elect Engn Keelung 202301 Taiwan Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung 202301 Taiwan

massive multiple-input multiple-output (MIMO) is a promising technology for enhancing quality of service in communication systems, but deploying numerous antennas increases detection complexity. To address this challenge, this paper introduces a novel detection scheme called DeepEigen-Tabu, combining the deep learning-based eigen network (DeepEigNet) with probabilistic Tabu search (P-TS). In the proposed scheme, DeepEigNet, a deep neural network, is constructed to utilize the eigenvalues and eigenvectors of the channel matrix to provide approximate symbol estimates. Subsequently, these estimates serve as the initialization and are prioritized according to their probabilities of correction to support the P-TS. Furthermore, the P-TS integrates an early stopping mechanism based on correction probabilities to eliminate unnecessary iterations during the Tabu search process. Finally, computer simulations and complexity analysis demonstrate that the proposed DeepEigen-Tabu scheme outperforms existing methods while maintaining lower complexity. For instance, in communication scenarios with both transmit and receive antennas set to 16, the proposed DeepEigen-Tabu method demonstrates savings of approximately signal-to-noise ratio (SNR) 0.8 dB at bit error rate (BER) 10(-3), compared to existing approaches in 4-ary quadrature amplitude modulation (4-QAM) symbol modulation. When the number of antennas is increased to 24 and using 16-QAM, the proposed DeepEigen-Tabu provides an improvement of 0.5 dB in SNR performance. Specifically, the proposed DeepEigen-Tabu not only achieves superior performance, as mentioned earlier, but also incurs a lower computational cost. The performance enhancements can be attributed to the DeepEigNet's provision of effective initialization, along with the early stopping and efficient candidate movement mechanisms employed by the P-TS method.

关键词： Symbols massive MIMO Vectors Signal to noise ratio Reliability Detectors Bit error rate massive multiple-input multiple-output deep learning Tabu search symbol detection

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Secure communication of intelligent reflecting surface-aided NOMA in massive MIMO networks

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COMPUTER COMMUNICATIONS 2024年 225卷 229-238页

作者： Hazrati, Bahar KN Toosi Univ Technol Dept Elect Engn Tehran Iran

A Non-Orthogonal multiple Access (NOMA) network, in which the base station (BS) incorporates massive multiple-input multiple-output (MIMO) technology, is considered in this paper. This research study focuses on investigating physical layer security in this network when a jammer is present, leveraging intelligent Reflecting Surface (IRS) technology. The IRS is an innovative approach strategically implemented to enhance communication quality by assisting distant users in establishing a reliable connection with the BS. Two key metrics in physical layer security are evaluated: the secrecy rate (SR) for pairs of NOMA users and the secrecy outage probability (SOP). Additionally, the impact of using a jammer is assessed by comparing the network's performance with and without a jammer. The results indicate that by increasing in the antenna numbers, the rate of secrecy is improved, and the SOP is decreased. Moreover, as the transmit signal-to-noise ratio (SNR) increases, the SR is enhanced, but the SOP is degraded. However, the increase in the IRS element numbers results in a tendency for the SOP to rise. Furthermore, it is evident that incorporating a jammer improves the network's performance.

关键词： Physical layer security massive multiple-input multiple-output Non-orthogonal multiple access Intelligent reflecting surface

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Deep-SOR detection for massive MIMO systems

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2025年 197卷

作者： Lu, Hoang-Yang Azizi, S. Pourmohammad Cheng, Shyi-Chyi Natl Taiwan Ocean Univ Dept Elect Engn Keelung Taiwan Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung Taiwan

In massive MIMO systems, particularly in highly loaded scenarios where the number of transmit antennas approaches that of receive antennas, symbol detection faces significant challenges, including increased computational complexity and degraded performance. To address these issues, in the paper we propose a deep learning (DL)-assisted successive over-relaxation (SOR) detector. This detector utilizes two relaxation vectors to enhance performance, which are determined through DL training. Additionally, we introduce a convergence theorem and conduct simulations to validate their determination. Finally, simulation and complexity analysis results demonstrate that the proposed detector achieves superior performance with a moderate computational cost, especially in highly loaded scenarios.

关键词： massive multiple-input multiple-output Deep learning Successive over-relaxation

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Decorrelation Lenses for massive MIMO Visible Light Communications

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IEEE PHOTONICS TECHNOLOGY LETTERS 2025年第10期37卷 575-578页

作者： Xia, Jiliang Zhu, Bingcheng Zhang, Zaichen Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

Visible light communications (VLC) have garnered increasing attention due to its high data rate, large capacity and enhanced security. However, current VLC systems suffer from limited bandwidth of off-the-shelf light-emitting-diodes (LEDs), and it is still challenging to realize a large number of parallel VLC channels for higher capacity with compact transceivers due to the inter-link interferences. This letter presents a novel decorrelation lens system for cost-effective and compact parallel VLC links. The system is designed to address the challenges associated with densely packed LEDs, where signal separation is critical for minimizing the inter-link interference. We derive the optimal positions of lenses to minimize the receiver size. Average received power and bit error rate are expressed in closed forms. Simulation results demonstrate that massive multiple-input multiple-output communications with densely packed LEDs can be realized when the lenses and photodiode plane are properly placed.

关键词： Lenses Light emitting diodes Visible light communication Imaging Optical imaging Optical receivers massive MIMO Decorrelation Bit error rate Simulation Decorrelation lenses massive multiple-input multiple-output visible light communication

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