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An Efficient Convex-Hull Relaxation Based Algorithm for Multi-User Discrete Passive Beamforming

IEEE signal processing LETTERS 2024年 31卷 2275-2279页

作者： Lai, Wenhai Wu, Zheyu Feng, Yi Shen, Kaiming Liu, Ya-Feng Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China Chinese Acad Sci Inst Computat Math & Sci Engn Comp Acad Math & Syst Sci State Key Lab Sci & Engn Comp Beijing 100190 Peoples R China

Intelligent reflecting surface (IRS) is an emerging technology to enhance spatial multiplexing in wireless networks. This letter considers the discrete passive beamforming design for IRS in order to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among multiple users in an IRS-assisted downlink network. The main design difficulty lies in the discrete phase-shift constraint. Differing from most existing works, this letter advocates a convex-hull relaxation of the discrete constraints which leads to a continuous reformulated problem equivalent to the original discrete problem. This letter further proposes an efficient alternating projection/proximal gradient descent and ascent algorithm for solving the reformulated problem. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods significantly.

关键词： Array signal processing signal to noise ratio Interference Downlink signal processing algorithms Minimax techniques Indexes Convex-hull relaxation discrete passive beamforming intelligent reflecting surface (IRS)

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Sparse DOA Estimation With Polarimetric Arrays

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 886-901页

作者： Aubry, Augusto Boddi, Marco De Maio, Antonio Rosamilia, Massimo Univ Napoli Federico II Dept Elect Engn & Informat Technol DIETI I-80125 Naples Italy

This paper addresses the Direction-of-Arrival (DOA) estimation problem using a narrowband polarimetric array sensing system. The considered receiving equipment is composed of two sub-arrays of sensors with orthogonal polarizations. By suitably modeling the received signal via a sparse representation (accounting for the multiple snapshots and the polarimetric array manifold structure), two iterative algorithms, namely Polarimetric Sparse Learning via Iterative Minimization (POL-SLIM) and Polarimetric Sparse Iterative Covariance-based Estimation (POL-SPICE), are devised to accomplish the estimation task. The proposed algorithms provide accurate DOA estimates while enjoying nice (rigorously proven) convergence properties. Numerical analysis shows the effectiveness of POL-SLIM and POL-SPICE to successfully locate signal sources in both passive sensing applications (with large numbers of collected snapshots) and radar spatial processing, also in comparison with single-polarization counterparts as well as theoretical benchmarks.

关键词： Direction-of-arrival estimation Vectors Atoms signal processing algorithms Sensors Maximum likelihood estimation Covariance matrices DOA estimation high resolution polarimetry sparse methods

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Convergence of Expectation-Maximization Algorithm With Mixed-Integer Optimization

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IEEE signal processing LETTERS 2024年 31卷 1229-1233页

作者： Joseph, Geethu Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands

The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables). The requirement is not met when parameters comprise both discrete and continuous variables, making the convergence analysis nontrivial. This paper introduces a set of conditions that ensure the convergence of a specific class of EM algorithms that estimate a mixture of discrete and continuous parameters. Our results offer a new analysis technique for iterative algorithms that solve mixed-integer non-linear optimization problems. As a concrete example, we prove the convergence of an existing EM-based sparse Bayesian learning algorithm that estimates the state of a linear dynamical system with jointly sparse inputs and bursty missing observations. Our results establish that the algorithm converges to the set of stationary points of the maximum likelihood cost with respect to the continuous optimization variables.

关键词： Convergence signal processing algorithms Heuristic algorithms Sparse matrices Maximum likelihood estimation Bayes methods Vectors Discrete non-linear optimization global convergence theorem sparse Bayesian learning bursty missing data

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A Consumer-Oriented Image Transformation Scheme With a Secret Key for Privacy Protection

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IEEE signal processing LETTERS 2024年 31卷 2280-2284页

作者： Zhao, Ruoyu Wen, Wenying Zhu, Youwen Lan, Rushi Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

Images in electronic devices may pose privacy threats since they can capture sensitive information about consumers. Meanwhile, face recognition (FR) systems are widely used, exacerbating these concerns. Some learning-based schemes have been proposed to protect the privacy of facial images. However, consumers might encounter challenges in implementing them due to specific requirements related to computing power and professional background. The non-learning semantic adversarial perturbation schemes address the aforementioned issues, but they are either irreversible or necessitate additional storage space. To this end, we propose a consumer-oriented image transformation scheme that can prevent the recognition of images by FR systems. It offers a consumer-friendly method compared to schemes that need high-performance equipment and specialized knowledge. The proposed scheme is implemented by reversible embedding the key to coefficients during the JPEG compression. The proposed scheme can be operated in general computing devices by ordinary consumers. The experiments demonstrated that facial images can be protected by the proposed scheme.

关键词： Privacy signal processing algorithms Semantics Perturbation methods Vectors Transform coding Protection Consumer friendliness facial image image transformation JPEG compatibility privacy preservation

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Reinforcement Learning Power Control Algorithm Based on Graph signal processing for Ultra-Dense Mobile Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第3期8卷 2694-2705页

作者： Li, Yujie Tang, Zhoujin Lin, Zhijian Gong, Yanfei Du, Xiaojiang Guizani, Mohsen Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Educ Beijing 100101 Peoples R China Fuzhou Univ Fac Comp Sci Fuzhou 350108 Fujian Peoples R China China Acad Informat & Communt Technol Beijing 100191 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar

Ultra-dense mobile networks (UDMNs) represent a promising technology for improving the network performance and providing the ubiquitous network accessibility in the beyond 5 G (B5G) mobile networks. Heterogenous densely deployed networks can dynamically offer high spectrum efficiency and enhance frequency reuse, which ultimately improves quality of service (QoS) and the user experience. However, mass inter- or intra-cell interference generated from overlap between small cells greatly limits network performance, especially when there is mobility between UEs and access points (APs). Even so, when network density increases, the complexity of conventional allocation methods can increase also. In this paper, we investigate a power control of downlink (DL) connection in the UNMNs with different types of APs. We propose a reinforcement learning (RL) power allocation algorithm based on graph signal processing (GSP) for ultra-dense mobile networks. Firstly, we construct a realistic system model under ultra-dense mobile networking, which includes the system channel mode and instantaneous rate. Then we employ a GSP tool to analyze network interference, the interference analysis results for the entire network are obtained to determine optimal RL power allocation. Finally, simulation results indicate that the proposed RL power control algorithm outperforms baseline algorithms when applied to a ultra-dense mobile networks.

关键词： Interference Resource management Power control signal processing algorithms Complexity theory Clustering algorithms Heuristic algorithms B5G graph signal processing power control reinforcement learning ultra-dense mobile networks

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A New Switching MVC Algorithm for Active Impulsive Noise Control

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IEEE EMBEDDED SYSTEMS LETTERS 2024年第3期16卷 271-274页

作者： Maya, Xochitl Soto, Adrian E. Vazquez, Angel A. Avalos, Juan G. Sanchez, Giovanny Sanchez, Juan C. Inst Politecn Nacl ESIME Culhuacan Mexico City 04440 Mexico

In realistic sound scenarios, cutting-edge active noise control (ANC) systems still suffer critical instabilities since these systems works under impulsive noise signals. To decrease the negative impact of this noise, several authors have made extraordinary efforts to develop efficient algorithms in terms of convergence speed and misadjustment. However, these algorithms still exhibits limited convergence capabilities. In this letter, we present for the first time, the development of a switching algorithm based on the maximum versoria criterion (MVC) algorithm. Our results demonstrate that the proposed switching algorithm exhibits good convergence properties while maintaining a lower-computational cost when compared with existing algorithms.

关键词： Filtering algorithms Adaptive filters Finite impulse response filters Switches Convergence Computational efficiency signal processing algorithms Active noise control (ANC) adaptive filtering impulsive noise maximum versoria criterion (MVC) algorithm switching algorithm

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Fishing Net Optimization: A Learning Scheme of Optimizing Multi-Lateration Stations in Air-Ground Vehicle Networks

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IEEE signal processing LETTERS 2024年 31卷 2965-2969页

作者： Zhao, Haitao Zhao, Chunxi Zhang, Tianyu Xu, Bo Sun, Jinlong Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing Peoples R China

Integrated sensing and communication in 6G, particularly for air-ground surveillance using automatic dependent surveillance-broadcast (ADS-B) and multi-lateration (MLAT) systems, is gaining significant research interest. This letter investigates the problem of optimal anchor station selection for tracking aerial vehicles, and proposes a novel heuristic learning scheme termed as fishing net-like optimization (FNO). Specifically, we perform constrained random walk steps on a two-dimensional surface to optimize the initial anchor stations' parameters. FNO also incorporates with new evaluation strategies and acceleration techniques to accelerate the convergence speed. Experimental results demonstrate that FNO can achieve better selection of the anchor stations, and the accuracy of the chosen MLAT can be improved by ten times or more with the anchors optimization.

关键词： Trajectory Convergence Optimization Surveillance Air to ground communication signal processing algorithms Indexes Sensors Location awareness Sun Anchor station selection heuristic learning multi-lateration (MLAT) optimization ADS-B

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signal processing for Single Biomolecule Identification Using Nanopores: A Review

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IEEE SENSORS JOURNAL 2021年第11期21卷 12808-12820页

作者： Das, Naren Mandal, N. Sekhar, Praveen Kumar RoyChaudhuri, Chirasree Deemed Univ Koneru Lakshmaiah Educ Fdn Elect & Commun Engn Dept Guntur 522502 Andhra Pradesh India IIEST Elect & Telecommun Engn Dept Howrah 711103 India Washington State Univ Sch Engn & Comp Sci Vancouver WA 98686 USA IIEST Ctr Healthcare Sci & Technol Howrah 711103 India

Nanopore sensors provide an innovative platform for rapid and label-free biomolecule detection. The analytical information extracted from resistive pulse sensing (RPS) measurement, though powerful has several limitations. To address these challenges, various signal conditioning algorithms have been researched which include efficient clustering and classification of data, applying probabilistic models to reduce information overlap and also leveraging transient analysis of ionic current instead of the steady state RPS measurement. Additionally, as the current is typically in few picoampere regime with high transition rates, its faithful measurement requires precision instrumentation like a patch clamp amplifier with low noise and high bandwidth. However, for practical deployment of such systems, it is necessary to develop portable electronic interface. Significant efforts have been directed globally to develop on-chip customized integrated circuits with low noise, high bandwidth amplifiers. Thus, it is absolutely timely and pertinent to conduct a review on the various signal processing schemes and electronic interfaces for single biomolecule identification using nanopores which will enable to comprehend the current status for its deployment as a commercial platform for genomics and ultrasensitive detection. To the best of our knowledge, the data extraction methodologies and electronic interface schemes for nanopores have not yet been summarized. In this paper, a comprehensive review emphasizing the signal processing algorithms for DNA sequencing and protein identification in complex analyte using functionalized and non-functionalized nanopores has been presented along with the on-chip low noise electronic interfaces required for field deployable measurement.

关键词： Sensors Data mining Current measurement Bandwidth Nanobioscience signal processing algorithms Noise measurement Nanopores single molecule sensing algorithm electronic interface

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On Minimizing the Probability of Large Errors in Robust Point Cloud Registration

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 39-47页

作者： Efraim, Amit Francos, Joseph M. Ben Gurion Univ Negev Dept Elect & Comp Engn IL-84105 Beer Sheva Israel

In solving a model fitting problem, the existence of outliers in the set of measurements can have a devastating effect on the solution accuracy. Traditionally, in order to overcome this problem, robust point cloud registration algorithms are composed of transformation hypothesis generation, followed by hypothesis evaluation aimed at selecting the best hypothesized estimate. Hypotheses evaluation is commonly performed using the sample consensus criterion. However, since this criterion accounts only for the consensus size, it fails when the maximal sample consensus is incorrect. We propose a new hypothesis evaluation approach, generalizing the sample consensus approach, where instead of the sample consensus, the transformation that maximizes the point clouds feature correlation is selected as the best hypothesis. The feature vector at each point contains information such as on local geometry and semantic context. Utilizing this information in the hypotheses evaluation and selection procedure allows for a correct decision even when the hypothesis yielding the maximal sample consensus is false. Consequently, the probability of selecting the correct model increases. We show both mathematically and empirically that substituting the sample consensus criterion with the proposed point cloud feature correlation hypothesis test (PC-FCHT) lowers the probability of large registration errors, compared to using the special case of sample consensus. The proposed PC-FCHT is applicable to any algorithm that follows the hypothesis generation and evaluation scheme, potentially improving the success rates of a wide family of point cloud registration algorithms.

关键词： Point cloud compression Correlation signal processing algorithms Feature extraction Three-dimensional displays signal processing Robustness Hypothesis evaluation point clouds registration robust estimation

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The Hitchhiker's Guide to Bias and Fairness in Facial Affective signal processing: Overview and techniques

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IEEE signal processing MAGAZINE 2021年第6期38卷 39-49页

作者： Cheong, Jiaee Kalkan, Sinan Gunes, Hatice Univ Cambridge Dept Comp Sci & Technol Cambridge CB3 0FD England Middle East Tech Univ Dept Comp Engn TR-06800 Ankara Turkey

Given the increasing prevalence of facial analysis technology, the problem of bias in the tools is now becoming an even greater source of concern. Several studies have highlighted the pervasiveness of such discrimination, and many have sought to address the problem by proposing solutions to mitigate it. Despite this effort, to date, understanding, investigating, and mitigating bias for facial affect analysis remain an understudied problem. In this work we aim to provide a guide by 1) providing an overview of the various definitions of bias and measures of fairness within the field of facial affective signal processing and 2) categorizing the algorithms and techniques that can be used to investigate and mitigate bias in facial affective signal processing. We present the opportunities and limitations within the current body of work, discuss the gathered findings, and propose areas that call for further research.

关键词： Current measurement signal processing algorithms signal analysis Face recognition Facial features

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