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Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems

IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2022年第5期16卷 1040-1054页

作者： Teng, Boyu Yuan, Xiaojun Wang, Rui Jin, Shi Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 610000 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user. By establishing a probability transition model for user mobility, we develop a message-passing algorithm, termed the Bayesian user localization and tracking (BULT) algorithm, to estimate and track the user position and the angles-of-arrivals (AoAs) at the user in an online fashion. We also derive the Bayesian Cramer Rao bound (BCRB) to characterize the fundamental performance limit of the considered tracking problem. To improve the tracking performance, we optimize the beamforming design at the BS and the RISs to minimize the derived BCRB. Simulation results show that our BULT algorithm can perform close to the derived BCRB, and significantly outperforms the counterpart algorithms without exploiting the temporal correlation of the user location.

关键词： Location awareness Array signal processing Radar tracking MIMO communication Sensors signal processing algorithms Bayes methods Reconfigurable intelligent surface user localiza-tion user tracking MIMO message passing

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Discrete MMSE Precoding for Multiuser MIMO Systems With PSK Modulation

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第10期21卷 8817-8831页

作者： Lopes, Erico S. P. Landau, Lukas T. N. Pontifical Catholic Univ Rio de Janeiro Ctr Studies Telecommun BR-22453900 Rio De Janeiro Brazil

We propose an optimal MMSE precoding technique using quantized signals with constant envelope. Unlike the existing MMSE design that relies on 1-bit resolution, the proposed approach employs uniform phase quantization and the bounding step in the branch-and-bound method is different in terms of considering the most restrictive relaxation of the nonconvex problem, which is then utilized for a suboptimal design also. We also propose robust MMSE designs where we exploit second order statistics of a channel state information mismatch. Moreover, unlike prior studies, we propose three different soft detection methods and an iterative detection and decoding scheme that allow the utilization of channel coding in conjunction with low-resolution precoding. Besides an exact approach for computing the extrinsic information, we propose two approximations with reduced computational complexity. Numerical simulations show that utilizing the MMSE criterion instead of the established maximum-minimum distance to the decision threshold yields a lower bit-error-rate in many scenarios. Furthermore, when using the MMSE criterion, a smaller number of bound evaluations in the branch-and-bound method is required for low and medium SNR. Finally, results based on an LDPC block code indicate that the proposed receive processing schemes yield a lower bit-error-rate compared to the conventional design.

关键词： Precoding Quantization (signal) Wireless communication signal processing algorithms Modulation MIMO communication Iterative decoding Precoding low-resolution quantization phase quantization MIMO systems branch-and-bound methods MMSE constant envelope log-likelihood-ratios soft detection iterative detection and decoding

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GRACGE: Graph signal Clustering and Multiple Graph Estimation

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2015-2030页

作者： Yuan, Yanli Soh, De Wen Yang, Xiao Guo, Kun Quek, Tony Q. S. Singapore Univ Technol & Design Singapore Singapore Xidian Univ State Key Lab Integrated Serv Inst Informat Sci Xian 710071 Shaanxi Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

In graph signal processing (GSP), complex datasets arise from several underlying graphs and in the presence of heterogeneity. Graph learning from heterogeneous graph signals often results in challenging high-dimensional multiple graph estimation problems, and prior information regarding which graph the data was observed is typically unknown. To address the above challenges, we develop a novel framework called GRACGE (GRAph signal Clustering and multiple Graph Estimation) to partition the graph signals into clusters and jointly learn the multiple underlying graphs for each of the clusters. GRACGE advocates a regularized EM (rEM) algorithm where a structure fusion penalty with adaptive regularization parameters is imposed on the M-step. Such a penalty can exploit the structural similarities among graphs to overcome the curse of dimensionality. Moreover, we provide a non-asymptotic bound on the estimation error of the GRACGE algorithm, which establishes its computational and statistical guarantees. Furthermore, this theoretical analysis motivates us to adaptively re-weight the regularization parameters. With the adaptive regularization scheme, the final estimates of GRACGE will geometrically converge to the true parameters within statistical precision. Finally, experimental results on both synthetic and real data demonstrate the performance of the proposed GRACGE algorithm.

关键词： Laplace equations signal processing algorithms Clustering algorithms Estimation error Topology Convergence Learning systems Multiple graph learning graph signal processing (GSP) signal clustering regularized EM algorithm nonasymptotic statistical analysis

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Learned Factor Graphs for Inference From Stationary Time Sequences

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 366-380页

作者： Shlezinger, Nir Farsad, Nariman Eldar, Yonina C. Goldsmith, Andrea J. Ben Gurion Univ Negev Sch ECE IL-8400711 Beer Sheva Israel Ryerson Univ Dept CS Toronto ON M5B 2K3 Canada Weizmann Inst Sci Fac Math & CS IL-7610001 Rehovot Israel Princeton Univ Dept EE Princeton NJ 08544 USA

The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been derived to carry out inference at controllable complexity using recursive computations over the factor graph representing the underlying distribution. An alternative model-agnostic approach utilizes machine learning (ML) methods. Here we propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. In the proposed approach, neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence, rather than the complete inference task. By exploiting stationary properties of this distribution, the resulting approach can be applied to sequences of varying temporal duration. Learned factor graphs can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data, and can be applied to sequences of different lengths. Our experimental results demonstrate the ability of the proposed learned factor graphs to learn from small training sets to carry out accurate inference for sleep stage detection using the Sleep-EDF dataset, as well as for symbol detection in digital communications with unknown channels.

关键词： signal processing algorithms Inference algorithms Hidden Markov models Training Computational modeling Time series analysis Task analysis Factor graphs deep learning time sequences

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Efficient 3-D Imaging Algorithm for Layered Structures Based on Scanning 1-D MIMO Array

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 8845-8855页

作者： Deng, Guilin Deng, Bin Chen, Xu Ma, Zhaoyang Zeng, Yang Wang, Hongqiang Zhao, Jun Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China PLA 78156 Chongqing 400000 Peoples R China

Since its unique advantages in the tradeoff between data acquisition rate and hardware cost, the scanning 1-D multiple-input-multiple-output (MIMO) array setup has been widely used in nondestructive testing applications, especially for product quality control on the assembly line. In this article, two fast fully focused algorithms based on scanning 1-D MIMO array are developed for imaging half-space medium structures. Both of the algorithms are derived under the half-space Green's function, and are implemented through fast Fourier transform (FFT) and phase compensation. The only difference between these two algorithms is that the matrix multiplication and nonuniform FFT are, respectively, employed to reconstruct the range profile. Simulation and experimental results verify the effectiveness of the proposed algorithms in computation efficiency and image reconstruction quality.

关键词： Imaging MIMO communication Radar imaging signal processing algorithms Radar Scattering Transmitters 3-D imaging algorithm layered structures nonuniform fast Fourier transform (NUFFT) scanning 1D-MIMO array

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Generalized MBI Algorithm for Designing Sequence Set and Mismatched Filter Bank With Ambiguity Function Constraints

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2918-2933页

作者： Chen, Zihao Liang, Junli Wang, Tao Tang, Bo So, Hing Cheung Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

A sequence set with ambiguity function (AF) specifications is frequently required in multi-transmit active sensing systems which exploit waveform diversity. This paper formulates a new model to jointly design sequence set and mismatched filter bank with AF requirements, which is a generalization of the auto-AF and cross-AF adopted in the matched filter scheme to attain lower AF sidelobe levels with an increased degree-of-freedom. The aforementioned designs result in nonconvex and nonlinear high-order polynomial (HOP) optimization problems with HOP constraints. Although the maximum block improvement (MBI) method has exhibited the powerful HOP optimization ability to design a short sequence with slow-time AF, it cannot tackle HOP constraints and involves high-complexity tensor operations. To address these issues, we develop a generalized MBI method for the HOP constrained optimization formulations. In addition, the proposed algorithm significantly reduces the computational complexity via designing an equivalent polynomial function for the original multi-linear tensor function. Numerical results demonstrate the excellent performance of our design solutions.

关键词： Optimization Tensors signal processing algorithms Sensors Receivers Radar antennas signal to noise ratio Auto cross-ambiguity function (AAF CAF) generalized maximum block improvement (GMBI) high-order polynomial (HOP) constraints mismatched filter bank sequence set design tensor operation

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Mixed Near-Field and Far-Field Localization and Array Calibration With Partly Calibrated Arrays

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2105-2118页

作者： He, Jin Shu, Ting Li, Linna Truong, Trieu-Kien Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China Shanghai Aerosp Elect Commun Equipment Res Inst Shanghai 201109 Peoples R China

The problem of passive localization of mixed near-field (NF) and far-field (FF) source signals in the presence of array gain-phase uncertainties is addressed. A new algorithm is aimed to use partly calibrated nonuniform linear arrays (NLAs), in which only three sensors have been fully-calibrated. Most of the existing algorithms deal with this problem by exploiting uniform linear arrays (ULAs). Moreover, they assume a simplified source-array model, in which the propagation magnitude scaling is completely neglected and the spatial phase difference is approximated by Taylor's polynomial. As an opposite, the proposed algorithm is employed to accommodate a more general situation: the exact spatial geometries and nonuniform linear arrays. In the proposed algorithm, three cumulant matrices are firstly defined to construct two matrix pencils. Unambiguous range and angle parameter estimates of the NF sources are then obtained from the generalized eigenvalues of the two defined matrix pencils. After that, these estimates are utilized to calibrate array gain-phase errors. Finally, a spectrum-MUSIC like approach is applied to accomplish the angle estimation for the FF sources. The new algorithm is shown to be readily simple and effective and will be verified both mathematically and numerically.

关键词： Sensor arrays Noise measurement Location awareness Array signal processing signal processing algorithms Uncertainty Geometry Array signal processing source localization array array gain-phase uncertainty near-field far-field

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Joint Design of Periodic Binary Probing Sequences and Receive Filters for PMCW Radar

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 5996-6010页

作者： Chen, Yutao Lin, Ronghao Cheng, Yuanbo Li, Jian Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Joint design of binary probing sequences and receive filters is critical to the performance of low-cost phase-modulated continuous wave (PMCW) radar, which can play an important role in autonomous driving applications to mitigate mutual interferences. For many existing PMCW radar systems, matched filters are employed at the receiver and binary sequences with good periodic auto-correlation properties within a low correlation zone (LCZ) are chosen as probing sequences. In this paper, we first introduce a cyclic algorithm to design binary sequences for the case of using matched filters at the receivers. To further decrease the sidelobes of the periodic correlations between the binary probing sequences and receive filters, at the cost of a small loss in signal-to-noise ratio (SNR), we propose an algorithm for the joint design of binary probing sequences and mismatched receive filters. The proposed algorithms are also extended to take into account the Doppler shifts caused by moving targets. Moreover, the efficient implementations of these algorithms utilizing the fast Fourier transform (FFT) operations are presented, making it possible to use them to design probing sequences and receive filters with long lengths. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed algorithms.

关键词： Radar signal processing algorithms Measurement signal to noise ratio Doppler shift Correlation Receivers Binary sequences cyclic optimizations fast Fourier transform (FFT) joint transmitter-receiver design low correlation zone (LCZ) mismatched filter phase-modulated continuous wave (PMCW) radar zoned integrated sidelobe level (ZISL) zoned peak sidelobe level (ZPSL)

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Recursive Quadratic Filtering for Linear Discrete Non-Gaussian Systems Over Time-Correlated Fading Channels

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3343-3356页

作者： Wang, Shaoying Wang, Zidong Dong, Hongli Chen, Yun Alsaadi, Fuad E. Binzhou Univ Coll Sci Yantai 256603 Shandong Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Hangzhou Dianzi Univ Sch Automat Key Lab IoT & Informat Fus Technol Zhejiang Hangzhou 310018 Peoples R China King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

In this paper, the recursive quadratic filtering issue is addressed for a class of linear discrete-time systems with non-Gaussian noises over time-correlated fading channels. The time-correlated fading channel, whose fading coefficient is modeled by a dynamic process subject to non-Gaussian random disturbance, is adopted to better characterize the time-correlation nature of the communication channel. By resorting to the state/measurement augmentation approach, the underlying system is converted into an augmented one with respect to the aggregation of the original vectors and the second-order Kronecker powers. Accordingly, the focus of this paper is on the design of a recursive quadratic filtering algorithm in the minimum-variance framework. To be more specific, an upper bound is first ensured on the filtering error covariance by solving certain matrix difference equations, and such an upper bound is then minimized by choosing the proper gain parameters. Moreover, sufficient conditions are obtained to guarantee the mean-square boundedness of the filtering error. Finally, some numerical simulations are provided to illustrate the correctness and validity of our developed quadratic filtering algorithm.

关键词： Fading channels Noise measurement Kalman filters signal processing algorithms Heuristic algorithms Autoregressive processes Covariance matrices Linear stochastic systems quadratic filtering time-correlated fading channels non-Gaussian noises means quare boundedness artificial intelligence

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Deadbeat Control With Bivariate Online Parameter Identification for SPS-Modulated DAB Converters

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IEEE ACCESS 2022年 10卷 54079-54090页

作者： Duong, Tan-Quoc Choi, Sung-Jin Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea

Deadbeat control is considered an efficient method of controlling dual active bridge (DAB) converters among the different control methods presented in recent years. The conventional deadbeat control is heavily reliant on the precise values of the system model parameters. However, in DAB converters, system model parameters such as series inductance and output capacitance suffer from mismatches due to operating conditions, manufacturing tolerance, and aging. Thus, the inevitable result is degradation in the steady-state and dynamic performance of the output voltage. In order to compensate for this drawback of deadbeat control, this study proposes an adaptive online parameter identification approach for DAB converters operating under single phase-shift (SPS) modulation. From the matrix form of linear equations in deadbeat control, the least-squares analysis (LSA) approach is utilized to solve the solution by a simple 2-by-2 matrix inverse calculation. Thus, series inductance and output capacitance are identified straightforwardly. Meanwhile, the predicted value of the phase-shift ratio is updated using sampled measurement values in deadbeat control after every sampling step, which can control the output voltage. The benefits of the proposed algorithm are demonstrated by theoretical analysis, simulation, and experimental results under a variety of parameter mismatches and operational circumstances.

关键词： Bridge circuits Steady-state signal processing algorithms Parameter estimation Mathematical models Capacitors Transformers Deadbeat control dual active bridge least-squares analysis parameter identification single phase-shift

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