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Beamspace Channel Estimation via PARAFAC Decomposition for RIS Assisted Millimeter-Wave Multiuser MISO Communications

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第5期74卷 7594-7609页

作者： Guo, Xinying Xie, Zongyuan Zhang, Jiankang Chen, Sheng Zhu, Chunhua Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Bournemouth Univ Dept Comp & Informat Poole BH12 5BB England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

The reconfigurable intelligent surface (RIS) with massive low-cost passive reflecting elements integrated on a planar surface has the ability to favourably reconfigure the wireless propagation environment, thereby significantly improving the performance of wireless communication networks. In this work, we consider uplink (UL) channel estimation for the RIS assisted millimeter-wave multiuser multiple-input single-output beamspace system where the base station (BS) is equipped with lens antenna array. This channel state information (CSI) estimation task is extremely challenging for two reasons. First, the BS only has limited number of radio frequency chains but the size of beamspace channel is very large. Second, the number of passive components in the RIS is abundance but they lack signal processing capabilities. By exploiting the parallel factor (PARAFAC) decomposition of the received signals, we derive an iterative estimation algorithm, called unitary approximate message passing (UAMP), to accurately estimate the channels between the BS and the RIS as well as the channels between the RIS and the users. To guide the selection of the system parameters, we provide the uniqueness conditions for our PARAFAC decomposition based channel estimation. To theoretically verify the efficiency of our UAMP algorithm, the Cram & eacute;r-Rao bound (CRB) of the estimation is also derived. Besides, we investigate the achievable downlink (DL) sum rate for the channel estimation obtained by the proposed algorithm by using the maximum power beam selection, the optimized phase shift matrix and the zero forcing precoding. Extensive simulation results demonstrate the excellent mean squared error (MSE) performance of our UAMP estimation algorithm. In particular, for sufficiently high UL signal-to-noise ratio, the MSE of our channel estimation reaches the CRB. Simulation results also show that the DL sum rate achieved by the estimated CSI is very close to that obtained by the perfect CSI. T

关键词： Channel estimation Millimeter wave communication Tensors Antenna arrays signal processing algorithms Estimation MISO Lenses Sparse matrices Radio frequency Reconfigurable intelligent surface lens antenna array millimeter-wave beamspace channel estimation PARAFAC Cram & eacute r-Rao bound downlink sum rate

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Uplink Multiplexing of eMBB/URLLC Services Assisted by Reconfigurable Intelligent Surfaces

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IEEE COMMUNICATIONS LETTERS 2024年第9期28卷 2206-2210页

作者： de Souza, Joao Henrique Inacio Croisfelt, Victor Kotaba, Radoslaw Abrao, Taufik Popovski, Petar Univ Estadual Londrina Dept Elect Engn BR-86057970 Londrina Brazil Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

This letter proposes a scheme assisted by a reconfigurable intelligent surface (RIS) for efficient uplink traffic multiplexing between enhanced mobile broadband (eMBB) and ultra-reliable-low-latency communication (URLLC). The scheme determines two RIS configurations based only on the eMBB channel state information (CSI) available at the base station (BS). The first optimizes eMBB quality of service, while the second reduces eMBB interference in URLLC traffic by temporarily silencing the eMBB traffic. Numerical results demonstrate that this approach, relying solely on eMBB CSI and without BS coordination, can outperform the state-of-the-art preemptive puncturing by 4.9 times in terms of URLLC outage probability.

关键词： Payloads Quality of service Reconfigurable intelligent surfaces Switches signal processing algorithms Reconfigurable intelligent surface (RIS) enhanced mobile broadband (eMBB) ultra-reliable low-latency communications (URLLC) multiplexing ultra-reliable low-latency communications (URLLC) multiplexing

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Deep Learning-Based Robust Adaptive Algorithm for Graph signal Estimation With Trainable Parameters

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3628-3632页

作者： Zhao, Haiquan Li, Chengjin Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The adaptive algorithm used for graph signal processing (GSP) based on the least mean square (LMS) strategy has poor performance in impulse noise environment, while the generalized maximum correntropy criterion (GMCC)-based GSP LMS (GSP LMSGMCC) algorithm effectively solves the mentioned above problem and is applicable to Gaussian noise scenarios. However, the manual debugging process of the step size, kernel parameter and shape parameter of the GSP LMSGMCC algorithm is complex, and inappropriate parameters can adversely affect its performance. Moreover, the variable step size (VSS) and variable kernel width (VKW) versions of the GSP LMSGMCC algorithm has more complex parameters that need to be debugged. To solve the mentioned problems, based on deep learning, we extend the iterative formulation of the GSP LMSGMCC algorithm to a multilayer neural network with the mentioned three parameters trainable. The neural network is trained on a dataset constructed based on the Brazilian temperature dataset. Simulation results show that our proposed method not only circumvents the manual selection of the related parameters, but also achieves a better coordination between the steady-state error accuracy and convergence rate.

关键词： Neural networks Training signal processing algorithms Estimation Noise measurement Deep learning Shape graph signal processing generaized maximum correntropy criterion neural network trainable parameters

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Robust Widely Nonlinear Quaternion Volterra Adaptive Filter Based on Recursive General Barron Algorithm and its Performance Analysis

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3578-3582页

作者： Liu, Qianqian Yan, Han Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Peoples R China NYU Tandon Sch Engn Brooklyn NY 11201 USA

Recent developments in signal processing have led to the introduction of a novel set of algorithms known as the quaternion-valued second-order Volterra (QSOV) least mean square family, alongside an advanced algorithm termed the widely nonlinear QSOV recursive least square (WNL-QSOV-RLS). These innovative methods are adept at handling signals in 3D and 4D dimensions. Nonetheless, their effectiveness is often challenged in real-world conditions where the error sensor is subjected to impulsive or non-Gaussian noise. Under these circumstances, algorithms relying on the MSE criterion tend to underperform or even diverge. In response to these limitations, this brief introduces the General Barron cost function (GBF), leading to the creation of a novel adaptive filter - the widely nonlinear quaternion-valued second-order Volterra adaptive filter based on recursive GBF (WNL-QSOV-RGBF) algorithm. This brief includes an extensive steady-state analysis of the WNL-QSOV-RGBF algorithm. Furthermore, the efficacy of this newly proposed algorithm, along with its family, is rigorously evaluated through simulation-based system identification and wind prediction tests. These tests unequivocally demonstrate the superior performance of the WNL-QSOV-RGBF algorithm in environments plagued by impulsive noise, compared to existing methods. Complementary to the theoretical analysis, this brief also presents a corroborative simulation study to validate the performance metrics.

关键词： signal processing algorithms Quaternions Prediction algorithms Steady-state Cost function Adaptive filters Filtering algorithms Nonlinear graph filter general robust cost function graph signal processing general Barron

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Optimization of RIS-Aided MIMO-A Mutually Coupled Loaded Wire Dipole Model

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第3期13卷 726-730页

作者： El Hassani, H. Qian, X. Jeong, S. Perovic, N. S. Di Renzo, M. Mursia, P. Sciancalepore, V. Costa-Perez, X. CNRS Cent Supelec Lab Signaux & Syst F-91192 Gif Sur Yvette France CY Cergy Paris Univ ETIS UMR 8051 ENSEACNRS Cergy France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91192 Gif Sur Yvette France NEC Labs Europe D-69115 Heidelberg Germany I2cat Barcelona 08034 Spain

We consider a reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) system in the presence of scattering objects. The MIMO transmitter and receiver, the RIS, and the scattering objects are modeled as mutually coupled thin wires connected to load impedances. We introduce a novel numerical algorithm for optimizing the tunable loads connected to the RIS, which does not utilize the Neumann series approximation. The algorithm is provably convergent, has polynomial complexity with the number of RIS elements, and outperforms the most relevant benchmark algorithms while requiring fewer iterations and converging in a shorter time.

关键词： Optimization Wires MIMO communication Load modeling Electromagnetic scattering Covariance matrices signal processing algorithms Reconfigurable intelligent surface mutual coupling scattering objects loaded thin wires optimization

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Protein-Protein Interaction Prediction via Graph signal processing

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IEEE ACCESS 2021年 9卷 142681-142692页

作者： Colonnese, Stefania Petti, Manuela Farina, Lorenzo Scarano, Gaetano Cuomo, Francesca Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET I-00185 Rome Italy Sapienza Univ Rome Dept Comp Control & Management Engn DIAG I-00185 Rome Italy

This paper tackles the problem of predicting the protein-protein interactions that arise in all living systems. Inference of protein-protein interactions is of paramount importance for understanding fundamental biological phenomena, including cross-species protein-protein interactions, such as those causing the 2020-21 pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, it is relevant also for applications such as drug repurposing, where a known authorized drug is applied to novel diseases. On the other hand, a large fraction of existing protein interactions are not known, and their experimental measurement is resource consuming. To this purpose, we adopt a Graph signal processing based approach modeling the protein-protein interaction (PPI) network (a.k.a. the interactome) as a graph and some connectivity related node features as a signal on the graph. We then leverage the signal on graph features to infer links between graph nodes, corresponding to interactions between proteins. Specifically, we develop a Markovian model of the signal on graph that enables the representation of connectivity properties of the nodes, and exploit it to derive an algorithm to infer the graph edges. Performance assessment by several metrics recognized in the literature proves that the proposed approach, named GRAph signal processing Based PPI prediction (GRABP), effectively captures underlying biologically grounded properties of the PPI network.

关键词： Proteins signal processing Predictive models Eigenvalues and eigenfunctions Drugs Biological system modeling signal processing algorithms Protein-protein interaction Markov random field graph signal processing

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Recursive Constrained Sine Second-Order Error Promoting Adaptive Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第11期71卷 4773-4777页

作者： Chen, Yong Li, Yingsong Miao, Yongchun Zakharov, Yuriy Huang, Zhixiang Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Anhui Peoples R China Univ York Sch Phys Engn & Technol York YO10 5DD England

This brief proposes a recursive constrained sine second-order error promoting adaptive (RCSSOEPA) algorithm. Compared with classical recursive method, the RCSSOEPA algorithm can achieve better steady state performance in impulsive-noise. In general, the sine second-order error (SSOE) is constructed to devise a new recursive constrained adaptive-filtering within the least-squares framework for solving linear constrained optimization problems. The mean-square (MS) stability of the RCSSOEPA and its theoretical instantaneous MS deviation under Gaussian and non Gaussian noise are analyzed, numerically investigated and discussed in detail. Simulated results are reported to give a confirmation of the theoretical analysis, and show that the RCSSOEPA outperforms recent developed constrained adaptive filtering algorithms in the estimation misalignment and when used for system identification under impulsive-noise.

关键词： signal processing algorithms Optimized production technology Gaussian noise Convergence Adaptive systems System identification Steady-state Impulsive-noise recursive constrained adaptive filtering (RCAF) sine second-order error (SSOE)

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Octonion Phase Retrieval

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IEEE signal processing LETTERS 2024年 31卷 1615-1619页

作者： Jacome, Roman Mishra, Kumar Vijay Sadler, Brian M. Arguello, Henry Univ Ind Santander Bucaramanga 680002 Colombia Univ Texas Austin Austin TX 78712 USA

signal processing over hypercomplex numbers arises in many optical imaging applications. In particular, spectral image or color stereo data are often processed using octonion algebra. Recently, the eight-band multispectral image phase recovery has gained salience, wherein it is desired to recover the eight bands from the phaseless measurements. In this letter, we tackle this hitherto unaddressed hypercomplex variant of the popular phase retrieval (PR) problem. We propose octonion Wirtinger flow (OWF) to recover an octonion signal from its intensity-only observation. However, contrary to the complex-valued Wirtinger flow, the non-associative nature of octonion algebra and the consequent lack of octonion derivatives make the extension to OWF non-trivial. We resolve this using the pseudo-real-matrix representation of octonion to perform the derivatives in each OWF update. We demonstrate that our approach recovers the octonion signal up to a right-octonion phase factor. Numerical experiments validate OWF-based PR with high accuracy under both noiseless and noisy measurements.

关键词： Algebra Quaternions signal processing algorithms Extraterrestrial measurements Phase measurement Sensors Noise measurement Hypercomplex signal processing octonion optical imaging phase retrieval quaternion

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Simplified Set-Membership Augmented Affine Projection Generalized Maximum Complex Correntropy Criterion Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3623-3627页

作者： Zhao, Haiquan Long, Xiaoqiang Hou, Xinyan Wang, Chen Southwest Jiaotong Univ Sch Elect Engn Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China

In order to cope with the situation, the performance of the set-membership augmented affine projection algorithm deteriorates dramatically when the output signal contains impact noise, this brief applies the maximum complex correntropy criterion to the set-membership augmented affine projection algorithm. And since the maximum complex correntropy criterion is robust, it can enhance the ability of the algorithm to resist impact noise. Then to better deal with the problems caused by strongly correlated input signals, the simplified set-membership augmented affine projection maximum complex correntropy algorithm (SSM-AAPMC) is proposed in this brief. To extend the algorithm to a more general form, the simplified set-membership augmented affine projection generalized maximum complex correntropy algorithm (SSM-AAPGMC) is derived. Furthermore, the stability of the proposed algorithm as well as the computational complexity between the proposed and other algorithms are analyzed. Finally, the simulation results confirm that the proposed two algorithms can maintain good performance under impact noise.

关键词： signal processing algorithms Cost function Computational complexity Random variables Kernel Filtering algorithms Computational efficiency Affine projection maximum complex correntropy set-membership

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Spatial-Frequency-Based Selective Fixed-Filter Algorithm for Multichannel Active Noise Control

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IEEE signal processing LETTERS 2024年 31卷 2635-2639页

作者： Su, Xiruo Shi, Dongyuan Zhu, Zhijuan Gan, Woon-Seng Ye, Lingyun Zhejiang Univ Zhejiang Prov Key Lab Network Multimedia Technol Hangzhou 310058 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The multichannel active noise control (MCANC) approach is widely regarded as an effective solution to achieve a large noise cancellation zone in a complicated acoustic environment. However, the sluggish convergence and massive computation of traditional adaptive multichannel active control algorithms typically impede the MCANC system's practical applications. The recently developed selective fixed-filter method offers a way to decrease the computational load in real-time scenarios and enhance the reaction time. Nevertheless, this method is specifically designed for the single-channel ANC system and only considers the frequency information of the noise. This inevitably impacts the effectiveness of reducing noise from various directions, particularly in the MCANC system. Therefore, we proposed a spatial-frequency-based selective fixed-filter ANC technique that adopts the Bhattacharyya Distance Matching (SFANC-BdM). In our work, the BdM is a one-step spectra and is designed by calculating similarity of different data distribution. According to the most similar case, the corresponding control filter is then selected. By avoiding separately extracting the direction and frequency information, the proposed method significantly increases the algorithm's efficiency. Compared to the conventional SFANC method, it enables a more accurate filter choice and achieves better noise reduction.

关键词： Noise Filters signal processing algorithms Microphones Vectors Training Noise reduction Multi-channel ANC passive localization selective fixed-filter spatial active noise control (ANC)

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