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A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2024年第2期26卷 781-823页

作者： Zhou, Hao Erol-Kantarci, Melike Liu, Yuanwei Poor, H. Vincent Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To realize the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey of optimization techniques for RIS-aided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sumrate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining lowcomplexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RIS-aided applications towards 6G networks and identify future challenges.

关键词： Optimization Surveys Wireless networks signal processing algorithms Heuristic algorithms 6G mobile communication Tutorials Reconfigurable intelligent surfaces optimization model-based methods heuristics machine learning

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Efficient Surrogate Modeling Method for Evolutionary Algorithm to Solve Bilevel Optimization Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第7期54卷 4335-4347页

作者： Jiang, Hao Chou, Kang Tian, Ye Zhang, Xingyi Jin, Yaochu Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Bielefeld Univ Fac Technol Chair Nat Inspired Comp & Engn D-33619 Bielefeld Germany

The purpose of this study was to develop an evolutionary algorithm (EA) with bilevel surrogate modeling, called BL-SAEA, for tackling bilevel optimization problems (BLOPs), in which an upper level problem is to be solved subject to the optimality of a corresponding lower level problem. The motivation of this article is that the extensive lower level optimization required by each upper level solution consumes too many function evaluations, leading to poor optimization performance of EAs. To this end, during the upper level optimization, the BL-SAEA builds an upper level surrogate model to select several promising upper level solutions for the lower level optimization. Because only a small number of upper level solutions require the lower level optimization, the number of function evaluations can be considerably reduced. During the lower level optimization, the BL-SAEA constructs multiple lower level surrogate models to initialize the population of the lower level optimization, thus further decreasing the number of function evaluations. Experimental results on two widely used benchmarks and two real-world BLOPs demonstrate the superiority of our proposed algorithm over six state-of-the-art algorithms in terms of effectiveness and efficiency.

关键词： Optimization Statistics Sociology Linear programming Search problems Evolutionary computation signal processing algorithms Bilevel optimization problem (BLOP) evolutionary algorithm (EA) Kriging model surrogate model

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A Directional Generation Algorithm for SAR Image Based on Azimuth-Guided Statistical Generative Adversarial Network

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5406-5421页

作者： Peng, Guobei Liu, Ming Chen, Shichao Tao, Mingliang Li, Yiyang Xing, Mengdao Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China North Automat Control Technol Inst Taiyuan 030000 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

The high cost of acquiring synthetic aperture radar (SAR) images results in the problem of insufficient data, which limits the performance of deep learning-based automatic target recognition (ATR) models. To solve this problem, a directional generation algorithm for SAR image based on azimuth-guided statistical generative adversarial network (AGSGAN) is proposed in this paper. The proposed algorithm can not only generate images with similar statistical characteristics as the real SAR images, but also control the azimuth of the generated images. Considering that the statistical characteristics of SAR images are different at different azimuth, the proposed algorithm partitions the azimuth intervals of SAR image into adaptive azimuth intervals, and the statistical characteristics of images within each adaptive azimuth interval should be similar. Then, the proposed algorithm obtains the statistical characteristics of real images by using the G(0) distribution to fit the statistical distribution of images within each adaptive azimuth interval. Finally, the random noise sampled from the fitted G(0) distribution and the serial number of adaptive azimuth interval are inputted into AGSGAN. The images that are within a specified adaptive azimuth interval and have statistical characteristics similar to the real images are generated by AGSGAN. Experimental results show that the images generated by the proposed algorithm are more realistic in statistical characteristics, and can effectively improve the recognition accuracy of the deep learning-based SAR automatic target recognition model.

关键词： Radar polarimetry Generative adversarial networks Azimuth signal processing algorithms Synthetic aperture radar Noise Training Image recognition Target recognition Solid modeling Synthetic aperture radar (SAR) automatic target recognition (ATR) azimuth-guided statistical generative adversarial network (AGSGAN) adaptive azimuth interval G(0) distribution

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Adaptive Superpixel-Guided Non-Homogeneous Image Dehazing

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IEEE signal processing LETTERS 2025年 32卷 591-595页

作者： Zhang, Hao Lu, Ping Qi, Te Xu, Yan Zeng, Tieyong Chinese Univ Hong Kong Dept Math Hong Kong Peoples R China ZTE Corp Shenzhen 518057 Peoples R China

Image dehazing is regarded as a fundamental image processing task with a major impact on higher-level imaging tasks. Many existing haze removal methods are designed for homogeneous haze, but in real-world cases, the haze is normally non-homogeneous. Superpixels, which segment an image into a set of closely spaced regions, can be employed in real-world scenarios to deal with non-homogeneous haze. In our paper, an adaptive non-homogeneous image dehazing approach that utilizes the superpixel-guided algorithm is designed to segment different hazy regions. Considering that both ambient light and transmission map estimation have a significant impact on the results, our research focuses on the development of a variational dehazing model that takes into account non-uniform ambient light and non-uniform transmission maps to address varying levels of haze. A series of numerical results illustrate the superiority and efficacy of our method.

关键词： Scattering signal processing algorithms Mathematical models Atmospheric modeling Image segmentation Optimization Feature extraction Numerical models Lighting Estimation Image dehazing non-homogeneous superpixel algorithm

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MPHR: A Robust Algorithm for Estimating Nodal Head in Water Networks

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IEEE signal processing LETTERS 2025年 32卷 856-860页

作者： Maggang, Amin A. Tay, David B. Gong, Jinzhe Josey, Brendan M. Deakin Univ Sch IT Geelong Vic 3220 Australia Deakin Univ Sch Engn Geelong Vic 3220 Australia

The monitoring of the pressure, in terms of nodal head values, is an important task in water distribution networks, to ascertain the integrity of the components, e.g. pipes. The placement of sensors at desired locations, in realistic situations, is a challenging problem due to various practical constraints. In practice, nodal heads are estimated at locations with no sensors. In this work, we propose an algorithm, termed mean percolation head reconstruction (MPHR), for estimating the nodal heads. The iterative algorithm is inspired by concepts from graph signal processing (GSP), and consists of two stages. The first stage is equivalent to the percolation of known values across the network, and the second stage performs some form of constrained consensus computations. The algorithm is robust against uncertainty in the network parameters and suboptimal placement of sensors. Extensive simulations have demonstrated the superiority of MPHR compared to other proposed reconstruction algorithms.

关键词： signal processing algorithms Sensors Magnetic heads Pressure sensors Head Distribution networks Vectors Uncertainty Training signal processing Graph signal processing (GSP) graph signal reconstruction nodal head estimation water distribution network

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On Optimal Tracking of Rapidly Varying Telecommunication Channels

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2726-2738页

作者： Niedzwiecki, Maciej Gancza, Artur Gdansk Univ Technol Fac Elect Telecommun & Comp Sci Dept Signals & Syst PL-80233 Gdansk Poland

When parameters of mobile telecommunication channels change rapidly, classical adaptive filters, such as exponentially weighted least squares algorithms or gradient algorithms, fail to estimate them with sufficient accuracy. In cases like this, one can use identification methods based on explicit models of parameter changes such as the method of basis functions (BF). When prior knowledge about parameter changes is available the BF approach can be optimized. The paper presents and compares three locally optimized BF algorithms differing in computational requirements. It also demonstrates how the performance of the proposed algorithms can be enhanced in cases where prior knowledge depends on unknown and/or time-varying environmental factors.

关键词： Estimation signal processing algorithms Approximation algorithms Noise Vectors Trajectory Noise measurement Time-varying channels local basis function approach parameter tracking recursive estimation

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Centimeter-Level 3-D Mobile Online Visible Light Positioning System With Single LED Lamp

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IEEE INTERNET OF THINGS JOURNAL 2024年第1期11卷 418-429页

作者： Ma, Shuai Li, Bing Zhang, Guanjie Li, Hang Qiu, Chen Yu, Chuang Li, Shiyin Shen, Chao Peng Cheng Lab Shenzhen 518055 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Shenzhen Res Inst Big Data Dept Industrializat Ctr Shenzhen 518172 Guangdong Peoples R China Univ Manchester Dept Comp Sci Cognit Robot Lab Manchester M13 9PL England

In this article, we consider a practical indoor 3-D mobile online visible light positioning (VLP) system, where the orientation of the user equipment (UE) is arbitrary. Based on the received signal strength (RSS) of multiple photodetectors (PDs), we formulate the 3-D VLP problem as a nonlinear least squares (NLSs) optimization problem, and then propose a sequential quadratic programming (SQP) positioning algorithm to efficiently calculate UE's location. To obtain more accurate positioning solutions, we further leverage the advantages of deep learning and develop a stochastic gradient descent (SGD)-based VLP algorithm, and achieve an average positioning error of 1.77 cm, which significantly outperforms existing RSS VLP localization methods. Moreover, we design a 3-D mobile online VLP system prototype by using a portable RaspberryPi 4 Model B as the positioning signal processor and data memory, and establish the first publicly available 3-D VLP measured data set, including both RSS and orientation. The proposed positioning schemes are implemented and evaluated via the designed prototype system, which can achieve centimeter-level positioning accuracy (below 1 cm in certain condition).

关键词： Three-dimensional displays Receivers Location awareness signal processing algorithms Prototypes Solid modeling Light emitting diodes Arbitrary orientation received signal strength (RSS) visible light positioning (VLP)

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Indoor Vehicle Positioning for MIMO-OFDM WIFI Systems via Rearranged Sparse Bayesian Learning

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第7期23卷 7849-7864页

作者： Du, Jianhe Cao, Jiali Jin, Libiao Li, Shufeng Liu, Jianbo Gao, Feifei Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Dept Automat State Key Lab Intelligence Technol & Syst Beijing 100084 Peoples R China

In this paper, we propose a novel vehicle positioning method for commodity multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) WIFI systems in indoor parking lots. To address the limitation of the small number of WIFI antennas, the proposed method first utilizes signal model rearrangement techniques, in which the abundant carrier frequency resources of WIFI are expanded into space resources. Then a rearranged off-grid sparse Bayesian learning (ROG-SBL) algorithm is developed for parameters estimation to achieve vehicle positioning. Specifically, by resorting to the Bayesian inference and Newton method, the position-related parameters are estimated iteratively by fitting the channel state information (CSI) measurement model, and thus the vehicle positioning is realized according to the geometric relationship. Moreover, we derive the Cram & eacute;Rao bound (CRB) as a performance reference for the proposed algorithm. Compared with the existing algorithms, the proposed one improves the positioning performance of the vehicle with fewer carrier numbers and has more stable performance. Simulation results show that the performance curves of the proposed algorithm for parameters estimation are close to the corresponding CRBs, and the proposed algorithm can cope with more challenging cases when the line-of-sight (LOS) path does not exist.

关键词： Wireless fidelity signal processing algorithms Location awareness Bayes methods Estimation Wireless communication Parameter estimation Positioning WIFI sparse Bayesian CSI CRB

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Cross-Domain Recognition Algorithm for Fitness Activities and Action Frequency Based on Dual Attention Prototype Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第23期11卷 38925-38935页

作者： Tian, Yong Wei, Xue Ye, Yingying Wang, Ying Qiao, Runjie Ding, Xuejun Liaoning Normal Univ Sch Phys & Elect Technol Dalian 116029 Peoples R China Dongbei Univ Finance & Econ Sch Management Sci & Engn Dalian 116025 Peoples R China

With the acceleration of life pace and improvement of living standards, adhering to exercise and fitness has become a common way for modern individuals to pursue a healthy lifestyle. Some fitness activity recognition algorithms based on channel state information (CSI) have been proposed to monitor the fitness effect. However, action frequency is also crucial for fitness effectiveness. The existing algorithms only recognize fitness activities without recognizing action frequency, and can only achieve satisfactory recognition accuracy in the trained experimental scenarios. To address these issues, we propose a cross-domain recognition algorithm for fitness activities and action frequency based on dual attention prototype networks, abbreviated as CDR-DAPNet algorithm. This algorithm uses CSI-ratio method to eliminate the random offset of CSI phase information, and converts the phase information into the form of an image, which is input into the constructed cross-domain recognition network. In the proposed recognition network, we construct a feature extraction module with serial structure consisting of spatial attention mechanism, ResNet34 network, and channel attention mechanism, to extract and reconstruct features from CSI phase images. Then, a recognition module with parallel structure consisting of two prototype networks is built for recognizing fitness activities and action frequency. Experimental results demonstrate that the CDR-DAPNet algorithm achieves high average recognition accuracy within the domain, and has good cross-domain performance when the amount of training data is sufficient or the training is assisted with few samples from cross-domain scenes.

关键词： Accuracy Feature extraction Sensors Prototypes signal processing algorithms Monitoring Wireless fidelity Channel state information (CSI) cross-domain recognition dual attention fitness activities and action frequency prototype networks

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No-Regret Distributed Learning in Subnetwork Zero-Sum Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第10期69卷 6620-6635页

作者： Huang, Shijie Lei, Jinlong Hong, Yiguang Shanbhag, Uday V. Chen, Jie Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Dept Control Sci & Engn Shanghai 201804 Peoples R China Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA

In this article, we consider a distributed learning problem in a subnetwork zero-sum game, where agents are competing in different subnetworks. These agents are connected through time-varying graphs where each agent has its own cost function and can receive information from its neighbors. We propose a distributed mirror descent algorithm for computing a Nash equilibrium and establish a sublinear regret bound on the sequence of iterates when the graphs are uniformly strongly connected and the cost functions are convex-concave. Moreover, we prove its convergence with suitably selected diminishing step sizes for a strictly convex-concave cost function. We also consider a constant step-size variant of the algorithm and establish an asymptotic error bound between the cost function values of running average actions and a Nash equilibrium. In addition, we apply the algorithm to compute a mixed-strategy Nash equilibrium in subnetwork zero-sum finite-strategy games, which have merely convex-concave (to be specific, multilinear) cost functions, and obtain a final-iteration convergence result and an ergodic convergence result, respectively, under different assumptions.

关键词： Games Game theory Convergence Costs Cost function Mirrors signal processing algorithms Distributed mirror descent no-regret distributed learning subnetwork zero-sum game

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