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Priority-Aware Secure Precoding Based on Multi-Objective Symbol Error Ratio Optimization

IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第3期69卷 1912-1929页

作者： Zhang, Jiankang Chen, Sheng Wang, Fasong Ng, Soon Xin Maunder, Robert G. Hanzo, Lajos Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

The secrecy capacity based on the assumption of having continuous distributions for the input signals constitutes one of the fundamental metrics for the existing physical layer security (PHYS) solutions. However, the input signals of real-world communication systems obey discrete distributions. Furthermore, apart from the capacity, another ultimate performance metric of a communication system is its symbol error ratio (SER). In this article, we pursue a radically new approach to PHYS by considering rigorous direct SER optimization exploiting the discrete nature of practical modulated signals. Specifically, we propose a secure precoding technique based on a multi-objective SER criterion, which aims for minimizing the confidential messages' SER at their legitimate user, while maximizing the SER of the confidential messages leaked to the illegitimate user. The key to this challenging multi-objective optimization problem is to introduce a priority factor that controls the priority of directly minimizing the SER of the legitimate user against directly maximizing the SER of the leaked confidential messages. Furthermore, we define a new metric termed as the security-level, which is related to the conditional symbol error probability of the confidential messages leaked to the illegitimate user. Additionally, we also introduce the secure discrete-input continuous-output memoryless channel (DCMC) capacity referred to as secure-DCMC-capacity, which serves as a classical security metric of the confidential messages, given a specific discrete modulation scheme. The impacts of both the channel's Rician factor and the correlation factor of antennas on the security-level and the secure-DCMC-capacity are investigated. Our simulation results demonstrate that the proposed priority-aware secure precoding based on the direct SER metric is capable of securing transmissions, even in the challenging scenario, where the eavesdropper has three receive antennas, while the legitimate user only

关键词： Measurement Precoding Simulation Receiving antennas Physical layer security Security Optimization Physical layer security wiretap channel secrecy capacity secure precoding mean square error symbol error rate conditional error probability multiple-input multiple-output

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Transmit Antenna Selection for Space-Time Line Code Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第2期69卷 786-798页

作者： Lim, Seung-Chan Joung, Jingon Agcy Def Dev ADD Daejeon 34186 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea

In this study, we consider a space-time line code (STLC) system with N transmit antennas and two receive antennas. Although the STLC system is scalable to an arbitrary number of transmit antennas, a large number of them will increase the hardware cost and complicate the implementation of the system. To resolve this practical limitation, we propose a transmit antenna selection (TAS) scheme for the N-by-2 STLC system. If S <= N antennas are used for the transmission, the STLC transmitter selects the S antennas such that the instantaneous signal-to-noise ratio (SNR) is maximized. For a comprehensive performance analysis, the instantaneous SNR of the proposed TAS is characterized in terms of the statistical mean and moment-generating function (MGF). To investigate the performance of the proposed TAS approach, the approximated expression of error rate is derived based on the MGF method. Moreover, using asymptotic analysis, we show that the proposed TAS-STLC system achieves full spatial diversity of 2N, while the coding gain is affected by N, S, and the modulation order. As a special case, we also provide a mathematical analysis of the error rate of the conventional STLC system when N=S .

关键词： Transmitting antennas Receiving antennas Signal to noise ratio Error analysis MIMO communication Modulation Antenna selection diversity error rate multiple-input multiple-output space-time line code

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Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation in Grant-Free MIMO-NOMA

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DRONES 2023年第1期7卷 27-27页

作者： Chen, Shuo Li, Haojie Zhang, Lanjie Zhou, Mingyu Li, Xuehua Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Educ Beijing 100101 Peoples R China Baicells Technol Co Ltd Beijing 100094 Peoples R China

In the massive machine type of communication (mMTC), grant-free non-orthogonal multiple access (NOMA) is receiving more and more attention because it can skip the complex grant process to allocate non-orthogonal resources to serve more users. To address the limited wireless resources and substantial connection challenges, combining grant-free NOMA and multiple-input multiple-output (MIMO) is crucial to further improve the system's capacity. In the grant-free MIMO-NOMA system, the base station should obtain the relevant information of the user before data detection. Thus, user activity detection (UAD) and channel estimation (CE) are two problems that should be solved urgently. In this paper, we fully consider the sparse characteristics of signals and the spatial correlation between multiple antennas in the grant-free MIMO-NOMA system. Then, we propose a spatial correlation block sparse Bayesian learning (SC-BSBL) algorithm to address the joint UAD and CE problems. First, by fully mining the block sparsity of signals in the grant-free MIMO-NOMA system, we model the joint UAD and CE problem as a three-dimensional block sparse signal recovery problem. Second, we derive the cost function based on the hierarchical Bayesian theory and spatial correlation. Finally, to estimate the channel and the set of active users, we optimize the cost function with fast marginal likelihood maximization. The simulation results indicate that, compared with the existing algorithms, SC-BSBL can always fully use the signal sparsity and spatial correlation to accurately complete UAD and CE under various user activation probabilities, SNRs, and the number of antennas. The normalized mean square error of CE can be reduced to 0.01, and the UAD error rate can be less than 10(-5).

关键词： sparse Bayesian learning grant-free multiple-input multiple-output non-orthogonal multiple access channel estimation user active detection

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Feasibility Verification of ATSC 3.0 MIMO System for 8K-UHD Terrestrial Broadcasting

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IEEE TRANSACTIONS ON BROADCASTING 2021年第4期67卷 909-916页

作者： Jung, Hoiyoon Park, Sung-Ik Lim, Bo-Mi Kwon, Haechan Hur, Namho Jeon, Sungho Kim, Hyeongseok Kim, Jeongchang Elect & Telecommun Res Inst Media Broadcasting Res Sect Daejeon 34129 South Korea Korean Broadcasting Syst Media Transmiss Dept Seoul 07235 South Korea Korea Maritime & Ocean Univ Interdisciplinary Major Maritime AI Convergence Dept Elect & Commun Engn Busan 49112 South Korea

This paper presents feasibility verification of the Advanced Television Systems Committee (ATSC) 3.0 MIMO system for 8K-UHD terrestrial broadcasting services. For a reliable performance evaluation, computer simulation, laboratory test, and field trial are conducted, and the tests' results are compared with the same performance metrics. Several physical layer configurations with various data rates from 63 Mbps to 113 Mbps are verified. The field trial results confirm that the ATSC 3.0 MIMO system can support data rates up to 113 Mbps within a 6 MHz bandwidth.

关键词： MIMO communication Broadcasting Modulation Computer simulation Demodulation Transmitting antennas Transmitters ATSC 30 physical layer multiple-input multiple-output laboratory test field trial 8K-UHD

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Soft-output Successive Cancellation Stack Polar Decoder

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第6期70卷 6238-6243页

作者： Xiang, Luping Liu, Yusha Maunder, Robert G. Yang, Lie-Liang Hanzo, Lajos Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

Polar coding has been ratified for employment in the 3GPP New Radio standard and several soft-decision decoders achieved comparable performance to that of the state-of-the-art successive cancellation list decoder. Aiming for further improving the performance of the soft-decision polar decoders, we propose a soft-output successive cancellation stack (SSCS) polar decoder, which jointly exploits the benefits of the depth-first search of the stack decoder and the soft information output of the belief propagation decoder. This has the substantial benefit of facilitating soft-input soft-output (SISO) decoding and seamless iterative information exchange in turbo-style receivers. As a further contribution, we intrinsically amalgamate our SSCS decoder into polar-coded large-scale multiple-input multiple-output (MIMO) systems and conceive an iterative turbo receiver, operating on the basis of logarithmic likelihood ratios (LLRs). Our simulation results show that the proposed SSCS decoder is capable of outperforming the state-of-the-art SISO polar decoders, despite requiring a lower complexity at moderate to high signal-to-noise ratios (SNRs). Additionally, compared with the non-iterative hard-output SCS decoder, our SSCS scheme attained 1.5 dB SNR gain at a bit error ratio level of 10(-5), when decoding the [256,512] polar code of a (64 x 64) MIMO system.

关键词： Polar code successive cancellation stack decoding soft-input soft-output multiple-input multiple-output

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Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

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IEEE ACCESS 2021年 9卷 33704-33755页

作者： Davoli, Alessandro Guerzoni, Giorgio Vitetta, Giorgio M. Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari I-41125 Modena Italy

Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.

关键词： Deep learning Learning systems MIMO radar Estimation Signal processing algorithms Radar antennas Antenna arrays Radar multiple-input multiple-output machine learning beamforming micro-Doppler range-azimuth estimation

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Machine Learning-based MIMO Signal Detection in Wireless Networks with Random Traffic

Machine Learning-based MIMO Signal Detection in Wireless Net...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Lai, Po-Yen Gau, Rung-Hung Natl Yang Ming Chiao Tung Univ Inst Commun Engn Hsinchu Taiwan

ISBN: (纸本)9781665442664

In this paper, we propose a novel machine learning-based signal detection scheme for multi-user wireless multiple-input multiple-output (MIMO) networks with random traffic. We focus on the challenging case in which the number of active users that transmit data to the base station in a time slot is a random variable from the viewpoint of the base station. Instead of using multiple machine learning models and exhaustive search, we propose using a novel deep machine learning model that adopts an extended constellation diagram and a loss function based on the nonuniform probability mass function for transmitted symbols. Simulation results reveal that the proposed machine learning-based signal detection scheme outperforms the zero-forcing detector and the minimum mean square error detector in wireless MIMO networks when the number of active users is random.

关键词： multiple-input multiple-output wireless communications signal detection machine learning deep neural networks random traffic prior probability

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Magnitude and Wrap-Phase OFDM for MIMO Visible Light Communication Systems

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IEEE COMMUNICATIONS LETTERS 2021年第7期25卷 2324-2328页

作者： Al-Nahhal, Mohamed Basar, Ertugrul Uysal, Murat Ozyegin Univ Dept Elect & Elect Engn TR-34794 Istanbul Turkey Koc Univ Dept Elect & Elect Engn CoreLab TR-34450 Istanbul Turkey

Generalized light emitting diode (LED) index modulation (GLIM) has been proposed as an effective multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) scheme for visible light communications (VLC) systems. In this letter, we propose a magnitude and wrap-phase OFDM (MW-OFDM) scheme for MIMO VLC systems. The proposed MW-OFDM system relies on the conversion of complex signals into polar form with the magnitudes and wrap-phases to decrease the restriction on the number of LEDs compared to the conventional GLIM-OFDM. Furthermore, maximum likelihood and best linear unbiased estimators of the proposed scheme are derived. The proposed MW-OFDM scheme improves the average bit error rate and provides a significant reduction in the decoding complexity, compared to the GLIM-OFDM. Moreover, a half number of LEDs are required for the proposed scheme to deliver the same spectral efficiency in a comparison with the GLIM-OFDM. Numerical results are presented over MIMO VLC channels to elaborate the superiority of our proposed scheme.

关键词： Visible light communications multiple-input multiple-output orthogonal frequency division multiplexing maximum likelihood estimator decoding complexity

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3 x 3 MIMO Fiber-Wireless System in W-Band With WDM/PDM RoF Transmission Capability

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2021年第24期39卷 7794-7803页

作者： Dat, Pham Tien Rottenberg, Francois Kanno, Atsushi Yamamoto, Naokatsu Kawanishi, Tetsuya Natl Inst Informat & Commun Technol Network Res Inst Tokyo 1840015 Japan Catholic Univ Louvain ICTEAM Inst B-1348 Louvain La Neuve Belgium Waseda Univ Sch Fundamental Sci & Engn Tokyo 1698050 Japan

We propose and demonstrate a 3 x 3 full multiple-input multiple-output (MIMO) fiber-wireless system in the W-band with wavelength-division multiplexing (WDM) and polarization-division multiplexing (PDM) radio-over-fiber (RoF) transmission capability. The system is realized using two highly stable RoF links based on the optical self-heterodyne method and PDM transmission in one of the RoF links. We experimentally demonstrate and confirm satisfactory performances for a 3 x 3 MIMO offset quadrature amplitude modulation-based filter-bank multicarrier signal. Total capacities of approximately 110 and 132 Gb/s, corresponding to spectral efficiencies of approximately 8.5 and 10.2 bits/s/Hz, respectively, are achieved for the system using antennas placed in the same and alternately different polarizations, respectively. The system is applicable to WDM and combined WDM/PDM RoF transmissions. It provides a scalable solution for facilitating large-scale MIMO signal transportation and can be a promising solution for future mobile transport and radio access networks in high-frequency bands.

关键词： MIMO communication Optical fiber polarization Optical fiber networks Wavelength division multiplexing Interference Wireless communication Digital signal processing Fifth-generation and beyond millimeter-wave mobile fronthaul multiple-input multiple-output radio-over-fiber seamless fiber-wireless system

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Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded multiplexing Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Zhong, Chenxi Yang, Huiyuan Yuan, Xiaojun University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu China

The communication bottleneck of over-the-air federated learning (OA-FL) lies in aggregating the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradient aggregation by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. Based on the analysis results, we show that the optimal number of multiplexed data streams to minimize the upper bound on the FL learning performance loss is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem for the design of precoding and post-processing matrices to minimize the gradient aggregation error. To solve this problem, we develop an efficient algorithm based on alternating optimization (AO) and Karush-Kuhn-Tucker (KKT) conditions, which effectively mitigates the impact of the gradient aggregation error. Numerical results demonstrate the superb performance of the proposed SCoM approach. © 2002-2012 IEEE.

关键词： multiple-input multiple-output multiplexing Over-the-air federated learning turbo compressed sensing

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