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Cooperative Reflection and Synchronization Design for Distributed Multiple-RIS Communications

IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2022年第5期16卷 980-994页

作者： Zhao, Yaqiong Xu, Wei You, Xiaohu Wang, Ning Sun, Huan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 210096 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Huawei Technol Co Ltd Wireless Technol Lab Shanghai 201206 Peoples R China

To reap the promised gain achieved by distributed reconfigurable intelligent surfaces (RISs)-enhanced communications in a wireless network, timing synchronization among these metasurfaces is an essential prerequisite in practice. This paper proposes a unified framework for the joint estimation of the unknown timing offsets and the RIS channel parameters, as well as the design of cooperative reflection and synchronization algorithm for the distributed multiple-RIS communication. Considering that RIS is usually a passive device with limited capability of signal processing, the individual timing offset and channel gains of each hop of the RIS links cannot be directly estimated. To make the estimation tractable, we propose to estimate the cascaded channels and timing offsets jointly by deriving a maximum likelihood estimator. Furthermore, we theoretically characterize the Cramer-Rao lower bound (CRLB) to evaluate the accuracy of this estimator. By using the proposed estimator and the derived CRLBs, an efficient resynchronization algorithm is devised jointly at the RISs and the destination to compensate the multiple timing offsets. Based on the majorization-minimization framework, the proposed algorithm admits semi-closed and closed form solutions for the RIS reflection matrices and the timing offset equalizer, respectively. Simulation results verify that our theoretical analysis well matches the numerical tests and validate the effectiveness of the proposed resynchronization algorithm.

关键词： Timing Channel estimation Synchronization signal processing algorithms Equalizers Simulation Radio frequency Reconfigurable intelligent surface (RIS) timing offset multi-node synchronization channel estimation Cramer-Rao lower bound (CRLB)

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"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic signals

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 468-481页

作者： Weiss, Amir Nadler, Boaz MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Weizmann Inst Sci Dept Comp Sci & Appl Math Fac Math & Comp Sci IL-7610001 Rehovot Israel

We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizable non-linear Wiener-like filter as if the unknown deterministic signal were known. This leads to a threshold-type regularized estimator, where the threshold at each frequency is determined in a data-driven manner. We perform a theoretical analysis of our proposed estimator, and derive approximate formulas for its Mean Squared Error (MSE) at both low and high signal-to-Noise Ratio (SNR) regimes. We show that in the low SNR regime our method provides enhanced noise suppression, and in the high SNR regime it approaches the optimal unrealizable solution. Further, as we demonstrate in simulations, our solution is highly suitable for (approximately) bandlimited or frequency-domain sparse signals, and provides a significant gain of several dBs relative to other methods in the resulting MSE.

关键词： Deconvolution signal to noise ratio Optimized production technology signal processing algorithms Convolution Frequency estimation Noise measurement Deconvolution thresholding Wiener filter

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Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail

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IEEE signal processing MAGAZINE 2022年第6期39卷 76-84页

作者： Wu, Kai Zhang, J. Andrew Guo, Y. Jay UTS Global Big Data Technol Ctr Sydney NSW 2007 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia CSIRO Data61 Sydney NSW Australia Natl ICT Australia Networked Syst Sydney NSW Australia ZTE Corp Nanjing Peoples R China New South Wales Connect Innovat Network Sydney NSW Australia

Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, the Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, the Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1] , and the Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in physics [2] ; moreover, the Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing, and in artificial intelligence for defining neural networks.

关键词： Quantum computing Simulation Feature extraction Quantum mechanics signal processing algorithms Gaussian processes signal processing Neural networks Laser theory

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Sample-Based and Feature-Based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3832-3847页

作者： Cui, Ying Li, Yangchen Ye, Chencheng Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Federated learning (FL) has become a hot research area in enabling the collaborative training of machine learning models among multiple clients that hold sensitive local data. Nevertheless, unconstrained federated optimization has been studied mainly using stochastic gradient descent (SGD), which may converge slowly, and constrained federated optimization, which is more challenging, has not been investigated so far. This paper investigates sample-based and feature-based federated optimization, respectively, and considers both unconstrained and constrained nonconvex problems for each of them. First, we propose FL algorithms using stochastic successive convex approximation (SSCA) and mini-batch techniques. These algorithms can adequately exploit the structures of the objective and constraint functions and incrementally utilize samples. We show that the proposed FL algorithms converge to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective unconstrained and constrained nonconvex problems, respectively. Next, we provide algorithm examples with appealing computational complexity and communication load per communication round. We show that the proposed algorithm examples for unconstrained federated optimization are identical to FL algorithms via momentum SGD and provide an analytical connection between SSCA and momentum SGD. Finally, numerical experiments demonstrate the inherent advantages of the proposed algorithms in convergence speeds, communication and computation costs, and model specifications.

关键词： Optimization signal processing algorithms Stochastic processes Privacy Approximation algorithms Servers Computational modeling Federated learning nonconvex optimization stochastic optimization stochastic successive convex approximation

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On Non-Linear Trajectory Tracking via Variety-Based Background Subtraction Models

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 397-409页

作者： Elnakeeb, Amr Mitra, Urbashi Univ Southern Calif Elect Engn Los Angeles CA 90089 USA Univ Southern Calif Ming Hsieh Dept Elect Engn Los Angeles CA 90089 USA

Trajectory tracking wherein the trajectories can be described by polynomials in the spatial coordinate space is considered. A background subtraction model is extended to include an algebraic variety-based low rank constraint that captures the trajectory. The optimization is solved via the Iteratively Reweighted Least Squares method, adapted to the new signal model. The convergence of the Variety-based Background Subtraction Iterative Reweighted Least Squares (VBSI) algorithm is investigated, where a boundedness analysis is conducted. It is proved that the sequence of estimates generated by VBSI is bounded;furthermore, any limit point of that sequence is a stationary point of the optimization problem. An aggregated error function is shown to converge to zero. A bound on the estimation error is derived that characterizes the relationship between performance and system parameters. Simulation results show that the proposed algorithm achieves excellent recovery of the key matrices in both noisy and noiseless scenarios, and typically converges in 20 iterations. Furthermore, a performance improvement of 2.4 dB with regards to the Mean Square Error is observed over prior methods. Finally, the proposed technique is applied to real video data and shown to be effective in estimating non-linear trajectories.

关键词： Optimization Trajectory Sparse matrices signal processing algorithms Convergence Target tracking Analytical models Algebraic varieties background subtraction sparse low-rank object tracking

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Distributed Combined Acoustic Echo Cancellation and Noise Reduction in Wireless Acoustic Sensor and Actuator Networks

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 534-547页

作者： Ruiz, Santiago van Waterschoot, Toon Moonen, Marc Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium

The paper presents distributed algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor and actuator network (WASAN) where each node may have multiple microphones and multiple loudspeakers, and where the desired signal is a speech signal. A centralized integrated AEC and NR algorithm, i.e., multichannel Wiener filter (MWF), is used as starting point where echo signals are viewed as background noise signals and loudspeaker signals are used as additional input signals to the algorithm. By including prior knowledge (PK), namely that the loudspeaker signals do not contain any desired signal component, an alternative centralized cascade algorithm (PK-MWF) is obtained with an AEC stage first followed by an MWF-based NR stage which has a lower computational complexity. Distributed algorithms can then be obtained from the MWF and PK-MWF algorithm, i.e., the generalized eigenvalue decomposition (GEVD)-based distributed adaptive node-specific signal estimation (DANSE) and PK-GEVD-DANSE algorithm, respectively. In the former, each node performs a reduced dimensional integrated AEC and NR algorithm and broadcasts only 1 fused signal (instead of all its signals) to the other nodes. In the PK-GEVD-DANSE algorithm, each node performs a reduced dimensional cascade AEC and NR algorithm and broadcasts only 2 fused signals (instead of all its signals) to the other nodes. The distributed algorithms achieve the same performance, upon convergence, as the corresponding centralized integrated (MWF) and centralized cascade (PK-MWF) algorithm. It is observed, however, that the communication cost in the PK-GEVD-DANSE algorithm can also be reduced, where each node then broadcasts only 1 fused signal (instead of 2 signals) to the other nodes. The resulting algorithm, referred to as the pruned PK-GEVD-DANSE (pPK-GEVD-DANSE) algorithm, then effectively combines the lowest possible communication cost (as low as in the GEVD-DANSE algorithm) wit

关键词： Loudspeakers signal processing algorithms Microphones Acoustics Distributed algorithms Echo cancellers Speech processing Distributed signal processing wireless acoustic sensor and actuator networks acoustic echo cancellation noise reduction multichannel Wiener filter

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Polarization, Angle, and Delay Estimation for Tri-Polarized Systems in Multipath Environments

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第8期21卷 5828-5841页

作者： He, Jin Wang, Yijing Shu, Ting Truong, Trieu-Kien Shanghai Jiao Tong Univ Shanghai Key Lab Intelligent Sensing & Recognit Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

A new signal processing algorithm proposed here is aimed at estimating five-dimensional (5-D) polarization-space-time (polarization, angle, delay) channel parameters for a tri-polarized system that works in a multipath environment. Unlike most channel estimation algorithms, where spatially spread antenna arrays are deployed, our algorithm is designed for a spatial col-located tripole antenna. A third-order tensor model is firstly established for the multipath polarization-space-time channel. This tensor is subsequently solved by exploiting low-rank decomposition techniques. After that, a closed-form solution of 2-D angles, 2-D polarizations, and multipath delay estimates for each multipath ray are derived. The proposed algorithm requires no computation for iterative searching and parameter pairing, thus offering an advantage in time-sensitive applications. In addition, it is applicable to either narrowband or wideband systems of arbitrary bandwidth and center-frequency.

关键词： signal processing algorithms Estimation Antenna arrays Delay estimation Channel estimation Tensors MIMO communication Wireless communications channel estimation space-time-polarization parameter multipath channel

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User Activity Detection for Irregular Repetition Slotted Aloha Based MMTC

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3616-3631页

作者： Srivatsa, Chirag Ramesh Murthy, Chandra R. Indian Inst Sci Robert Bosch Ctr Cyber Phys Syst Bangalore 560012 Karnataka India Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 Karnataka India

Irregular repetition slotted aloha (IRSA) is a grant-free random access protocol for massive machine-type communications, in which users transmit replicas of their packet in randomly selected resource blocks within a frame. In this paper, we first develop a novel Bayesian user activity detection (UAD) algorithm for IRSA, which exploits both the sparsity in user activity as well as the underlying structure of IRSA transmissions. Next, we derive the Cramer-Rao bound (CRB) on the mean squared error in channel estimation. We empirically show that the channel estimates obtained as a by-product of the proposed UAD algorithm achieves the CRB. Then, we analyze the signal to interference plus noise ratio achieved by the users, accounting for UAD, channel estimation errors, and pilot contamination. Finally, we illustrate the impact of these non-idealities on the throughput of IRSA via Monte Carlo simulations. For example, in a system with 1500 users and 10% of the users being active per frame, a pilot length of as low as 20 symbols is sufficient for accurate user activity detection. In contrast, using classical compressed sensing approaches for UAD would require a pilot length of about 346 symbols. Our results reveal crucial insights into dependence of UAD errors and throughput on parameters such as the length of the pilot sequence, the number of antennas at the BS, the number of users, and the signal to noise ratio.

关键词： Channel estimation signal to noise ratio signal processing algorithms Throughput Contamination Decoding Symbols Channel estimation grant-free random access irregular repetition slotted aloha massive machine-type communications user activity detection

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Parameter Training Methods for Convolutional Neural Networks With Adaptive Adjustment Method Based on Borges Difference

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 673-685页

作者： Jian, Jing Gao, Zhe Kan, Tao Liaoning Univ Sch Math Shenyang 110036 Peoples R China Liaoning Univ Coll Light Ind Shenyang 110036 Peoples R China

This paper proposes a momentum algorithm based on Borges difference and an adaptive momentum (Adam) algorithm based on Borges difference to update parameters, which can adjust the momentum information more flexibly. The Borges difference is proposed from the definition of Borges derivative to be combined with the gradient algorithm in convolutional neural networks. The proposed momentum algorithm based on Borges difference and Adam algorithm based on Borges difference can be adjusted more flexibly in order to speed up the convergence. The parameter optimization algorithm with the Borges difference presents a better performance compared with the integer-order momentum algorithm and integer-order Adam algorithm, with the proposed nonlinear adjustment method for the parameter tuning of convolutional neural networks. By analyzing experimental results of Fashion-MNIST dataset and CIFAR-10 dataset, the optimization algorithms based on Borges difference proposed in this paper gain better effects on the optimization model compared with the corresponding ones based on the integer-order difference, and can speed up the convergence speed and recognition accuracy of the image recognition.

关键词： signal processing algorithms Optimization Neural networks Fractals Convergence Training Calculus Fractal difference convolution neural networks momentum Adam algorithm nonlinear adaptive tuning method

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Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2046-2061页

作者： Mohamadi, Niloofar Dong, Min Shahbaz Panahi, Shahram Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction.

关键词： Array signal processing Quality of service Multicast algorithms signal to noise ratio signal processing algorithms Interference Convergence Robust multicast beamforming large-scale systems alternating direction method of multipliers (ADMM) low-complexity convergence

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