This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures, by deploying graph convolutions over the topological links of the communication network. The proposed technique leverages only the local information available at each node and efficiently routes desired information to the destination nodes. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms. Numerical simulations depict the improved performance of the proposed method in training a GNN parameterization as compared to baseline algorithms.
{"title":"Learning State-Augmented Policies for Information Routing in Communication Networks","authors":"Sourajit Das;Navid NaderiAlizadeh;Alejandro Ribeiro","doi":"10.1109/TSP.2024.3516556","DOIUrl":"10.1109/TSP.2024.3516556","url":null,"abstract":"This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures, by deploying graph convolutions over the topological links of the communication network. The proposed technique leverages only the local information available at each node and efficiently routes desired information to the destination nodes. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms. Numerical simulations depict the improved performance of the proposed method in training a GNN parameterization as compared to baseline algorithms.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"204-218"},"PeriodicalIF":4.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1109/TSP.2024.3516083
Evan Becker;Parthe Pandit;Sundeep Rangan;Alyson K. Fletcher
Generative Adversarial Networks (GANs) are widely used for modeling complex data. However, the dynamics of the gradient descent-ascent (GDA) algorithms, often used for training GANs, have been notoriously difficult to analyze. We study these dynamics in the case where the discriminator is kernel-based and the true distribution consists of discrete points in Euclidean space. Prior works have analyzed the GAN dynamics in such scenarios via simple linearization close to the equilibrium. In this work, we show that linearized analysis can be grossly inaccurate, even at moderate distances from the equilibrium. We then propose an alternative non-linear yet tractable second moment model. The proposed model predicts the convergence behavior well and reveals new insights about the role of the kernel width on convergence rate, not apparent in the linearized analysis. These insights suggest certain shapes of the kernel offer both fast local convergence and improved global convergence. We corroborate our theoretical results through simulations.
{"title":"GAN Training With Kernel Discriminators: What Parameters Control Convergence Rates?","authors":"Evan Becker;Parthe Pandit;Sundeep Rangan;Alyson K. Fletcher","doi":"10.1109/TSP.2024.3516083","DOIUrl":"10.1109/TSP.2024.3516083","url":null,"abstract":"Generative Adversarial Networks (GANs) are widely used for modeling complex data. However, the dynamics of the gradient descent-ascent (GDA) algorithms, often used for training GANs, have been notoriously difficult to analyze. We study these dynamics in the case where the discriminator is kernel-based and the true distribution consists of discrete points in Euclidean space. Prior works have analyzed the GAN dynamics in such scenarios via simple linearization close to the equilibrium. In this work, we show that linearized analysis can be grossly inaccurate, even at moderate distances from the equilibrium. We then propose an alternative non-linear yet tractable <italic>second moment model</i>. The proposed model predicts the convergence behavior well and reveals new insights about the role of the kernel width on convergence rate, not apparent in the linearized analysis. These insights suggest certain shapes of the kernel offer both fast local convergence and improved global convergence. We corroborate our theoretical results through simulations.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"433-445"},"PeriodicalIF":4.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSP.2024.3515102
Xiaowei Qian;Xiaoling Hu;Chenxi Liu;Mugen Peng
The spectrum shift from sub-6G bands to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that the RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose a simple-yet-efficient wideband beamforming design for RIS-assisted systems, in which a transmitter sends wideband signals to a desired target with the aid of the RIS. In the proposed design, we exploit space-frequency Fourier transformation and stationary phase method to derive an approximate closed-form solution of RIS phase shifts, which significantly reduces the signal processing complexity compared to existing approaches. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve system performance in terms of communication rate and sensing resolution. Beyond generating the flat beampattern, we highlight that our proposed design is capable of mimicking any desired beampattern by matching the RIS phase shift with the amplitude modulation function, thus providing valuable insights into the design of novel wideband beamforming for RIS-assisted systems.
{"title":"Wideband Beamforming With RIS: A Unified Framework via Space-Frequency Transformation","authors":"Xiaowei Qian;Xiaoling Hu;Chenxi Liu;Mugen Peng","doi":"10.1109/TSP.2024.3515102","DOIUrl":"10.1109/TSP.2024.3515102","url":null,"abstract":"The spectrum shift from sub-6G bands to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that the RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose a simple-yet-efficient wideband beamforming design for RIS-assisted systems, in which a transmitter sends wideband signals to a desired target with the aid of the RIS. In the proposed design, we exploit space-frequency Fourier transformation and stationary phase method to derive an approximate closed-form solution of RIS phase shifts, which significantly reduces the signal processing complexity compared to existing approaches. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve system performance in terms of communication rate and sensing resolution. Beyond generating the flat beampattern, we highlight that our proposed design is capable of mimicking any desired beampattern by matching the RIS phase shift with the amplitude modulation function, thus providing valuable insights into the design of novel wideband beamforming for RIS-assisted systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"173-187"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSP.2024.3514676
Dunbiao Niu;Yiguang Hong;Enbin Song
In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a network. Our method is based on the Lagrange dual of an appropriately formulated primal problem created by introducing local variables for each agent and enforcing a consensus constraint among these variables. Due to the decomposed structure of the dual problem, the DINN method guarantees a superlinear (or even quadratic) convergence rate for both the primal and dual iteration sequences, achieving the same convergence rate as its centralized counterpart. Furthermore, by exploiting the special structure of the dual generalized Hessian, we design a distributed iterative method based on Nesterov's acceleration technique to approximate the dual Newton direction with suitable precision. Moreover, in contrast to existing second-order methods, the DINN method relaxes the requirement for the objective function to be twice continuously differentiable by using the linear Newton approximation of its gradient. This expands the potential applications of distributed Newton methods. Numerical experiments demonstrate that the DINN method outperforms the current state-of-the-art distributed optimization methods.
{"title":"A Dual Inexact Nonsmooth Newton Method for Distributed Optimization","authors":"Dunbiao Niu;Yiguang Hong;Enbin Song","doi":"10.1109/TSP.2024.3514676","DOIUrl":"10.1109/TSP.2024.3514676","url":null,"abstract":"In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a network. Our method is based on the Lagrange dual of an appropriately formulated primal problem created by introducing local variables for each agent and enforcing a consensus constraint among these variables. Due to the decomposed structure of the dual problem, the DINN method guarantees a superlinear (or even quadratic) convergence rate for both the primal and dual iteration sequences, achieving the same convergence rate as its centralized counterpart. Furthermore, by exploiting the special structure of the dual generalized Hessian, we design a distributed iterative method based on Nesterov's acceleration technique to approximate the dual Newton direction with suitable precision. Moreover, in contrast to existing second-order methods, the DINN method relaxes the requirement for the objective function to be twice continuously differentiable by using the linear Newton approximation of its gradient. This expands the potential applications of distributed Newton methods. Numerical experiments demonstrate that the DINN method outperforms the current state-of-the-art distributed optimization methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"188-203"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142805228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Graph Neural Networks (GNNs) have exhibited exceptional performance across diverse application domains by harnessing the inherent interconnectedness of data. Recent findings point towards instability of GNN under both feature and structure perturbations. The emergence of adversarial attacks targeting GNNs poses a substantial and pervasive threat, compromising their overall performance and learning capabilities. In this work, we first derive a theoretical bound on the global Lipschitz constant of GNN in the context of both feature and structure perturbations. Consequently, we propose a unifying approach, termed AdaLipGNN, for adversarial training of GNNs through an optimization framework which provides attack agnostic robustness. By seamlessly integrating graph denoising and network regularization, AdaLipGNN offers a comprehensive and versatile solution, extending its applicability and enabling robust regularization for diverse network architectures. Further, we develop a provably convergent iterative algorithm, leveraging block successive upper-bound minimization to learn robust and stable GNN hypothesis. Numerical results obtained from extensive experiments performed on real-world datasets clearly illustrate that the proposed AdaLipGNN outperforms other defence methods.
{"title":"A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks","authors":"Vipul Kumar Singh;Sandeep Kumar;Avadhesh Prasad;Jayadeva","doi":"10.1109/TSP.2024.3514091","DOIUrl":"https://doi.org/10.1109/TSP.2024.3514091","url":null,"abstract":"Graph Neural Networks (GNNs) have exhibited exceptional performance across diverse application domains by harnessing the inherent interconnectedness of data. Recent findings point towards instability of GNN under both feature and structure perturbations. The emergence of adversarial attacks targeting GNNs poses a substantial and pervasive threat, compromising their overall performance and learning capabilities. In this work, we first derive a theoretical bound on the global Lipschitz constant of GNN in the context of both feature and structure perturbations. Consequently, we propose a unifying approach, termed AdaLipGNN, for adversarial training of GNNs through an optimization framework which provides attack agnostic robustness. By seamlessly integrating graph denoising and network regularization, AdaLipGNN offers a comprehensive and versatile solution, extending its applicability and enabling robust regularization for diverse network architectures. Further, we develop a provably convergent iterative algorithm, leveraging block successive upper-bound minimization to learn robust and stable GNN hypothesis. Numerical results obtained from extensive experiments performed on real-world datasets clearly illustrate that the proposed AdaLipGNN outperforms other defence methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"83-98"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSP.2024.3514802
Jiaojiao Zhang;Xuechao He;Yue Huang;Qing Ling
This paper explores constrained non-convex personalized federated learning (PFL), in which a group of workers train local models and a global model, under the coordination of a server. To address the challenges of efficient information exchange and robustness against the so-called Byzantine workers, we propose a projected stochastic gradient descent algorithm for PFL that simultaneously ensures Byzantine-robustness and communication efficiency. We implement personalized learning at the workers aided by the global model, and employ a Huber function-based robust aggregation with an adaptive threshold-selecting strategy at the server to reduce the effects of Byzantine attacks. To improve communication efficiency, we incorporate random communication that allows multiple local updates per communication round. We establish the convergence of our algorithm, showing the effects of Byzantine attacks, random communication, and stochastic gradients on the learning error. Numerical experiments demonstrate the superiority of our algorithm in neural network training compared to existing ones.
{"title":"Byzantine-Robust and Communication-Efficient Personalized Federated Learning","authors":"Jiaojiao Zhang;Xuechao He;Yue Huang;Qing Ling","doi":"10.1109/TSP.2024.3514802","DOIUrl":"10.1109/TSP.2024.3514802","url":null,"abstract":"This paper explores constrained non-convex personalized federated learning (PFL), in which a group of workers train local models and a global model, under the coordination of a server. To address the challenges of efficient information exchange and robustness against the so-called Byzantine workers, we propose a projected stochastic gradient descent algorithm for PFL that simultaneously ensures Byzantine-robustness and communication efficiency. We implement personalized learning at the workers aided by the global model, and employ a Huber function-based robust aggregation with an adaptive threshold-selecting strategy at the server to reduce the effects of Byzantine attacks. To improve communication efficiency, we incorporate random communication that allows multiple local updates per communication round. We establish the convergence of our algorithm, showing the effects of Byzantine attacks, random communication, and stochastic gradients on the learning error. Numerical experiments demonstrate the superiority of our algorithm in neural network training compared to existing ones.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"26-39"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSP.2024.3499772
Ali Abdelbadie;Mona Mostafa;Salime Bameri;Ramy H. Gohary;Dimple Thomas
In [1], the legend of Figure 8 should have appeared as shown below.
在[1]中，图8的图例应该如下所示。
{"title":"Corrections to “DoA Estimation for Hybrid Receivers: Full Spatial Coverage and Successive Refinement”","authors":"Ali Abdelbadie;Mona Mostafa;Salime Bameri;Ramy H. Gohary;Dimple Thomas","doi":"10.1109/TSP.2024.3499772","DOIUrl":"10.1109/TSP.2024.3499772","url":null,"abstract":"In [1], the legend of Figure 8 should have appeared as shown below.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5353-5353"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10791305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSP.2024.3515008
Seonho Kim;Kiryung Lee
We consider a least absolute deviation (LAD) approach to the robust phase retrieval problem that aims to recover a signal from its absolute measurements corrupted with sparse noise. To solve the resulting non-convex optimization problem, we propose a robust alternating minimization (Robust-AM) derived as an unconstrained Gauss-Newton method. To solve the inner optimization arising in each step of Robust-AM, we adopt two computationally efficient methods. We provide a non-asymptotic convergence analysis of these practical algorithms for Robust-AM under the standard Gaussian measurement assumption. These algorithms, when suitably initialized, are guaranteed to converge linearly to the ground truth at an order-optimal sample complexity with high probability while the support of sparse noise is arbitrarily fixed and the sparsity level is no larger than $1/4$ . Additionally, through comprehensive numerical experiments on synthetic and image datasets, we show that Robust-AM outperforms existing methods for robust phase retrieval offering comparable theoretical performance guarantees.
{"title":"Robust Phase Retrieval by Alternating Minimization","authors":"Seonho Kim;Kiryung Lee","doi":"10.1109/TSP.2024.3515008","DOIUrl":"10.1109/TSP.2024.3515008","url":null,"abstract":"We consider a least absolute deviation (LAD) approach to the robust phase retrieval problem that aims to recover a signal from its absolute measurements corrupted with sparse noise. To solve the resulting non-convex optimization problem, we propose a robust alternating minimization (Robust-AM) derived as an unconstrained Gauss-Newton method. To solve the inner optimization arising in each step of Robust-AM, we adopt two computationally efficient methods. We provide a non-asymptotic convergence analysis of these practical algorithms for Robust-AM under the standard Gaussian measurement assumption. These algorithms, when suitably initialized, are guaranteed to converge linearly to the ground truth at an order-optimal sample complexity with high probability while the support of sparse noise is arbitrarily fixed and the sparsity level is no larger than \u0000<inline-formula><tex-math>$1/4$</tex-math></inline-formula>\u0000. Additionally, through comprehensive numerical experiments on synthetic and image datasets, we show that Robust-AM outperforms existing methods for robust phase retrieval offering comparable theoretical performance guarantees.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"40-54"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1109/TSP.2024.3512575
Anzheng Tang;Jun-Bo Wang;Yijin Pan;Wence Zhang;Yijian Chen;Hongkang Yu;Rodrigo C. de Lamare
In this paper, the channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of near-field (NF) spherical wavefront effects and spatially non-stationary (SnS) properties. Due to the diversity of SnS characteristics across different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase estimation scheme that decouples the problem into multiple subchannel estimation tasks. To solve these sub-tasks, we introduce a novel three-layer Bayesian inference scheme, exploiting the correlations and sparsity of the SnS subchannels in both the spatial and angular domains. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS properties in the spatial domain, and the third layer effectively decouples each subchannel from the observed signal. To enable efficient Bayesian inference, we develop a three-layer generalized approximate message passing (TL-GAMP) algorithm that combines structured variational message passing with belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed TL-GAMP algorithm, demonstrating its robustness across various channel environments, including NF-SnS, NF spatially stationary (NF-SS), and far-field spatially stationary (FF-SS) scenarios.
{"title":"Spatially Non-Stationary XL-MIMO Channel Estimation: A Three-Layer Generalized Approximate Message Passing Method","authors":"Anzheng Tang;Jun-Bo Wang;Yijin Pan;Wence Zhang;Yijian Chen;Hongkang Yu;Rodrigo C. de Lamare","doi":"10.1109/TSP.2024.3512575","DOIUrl":"10.1109/TSP.2024.3512575","url":null,"abstract":"In this paper, the channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of near-field (NF) spherical wavefront effects and spatially non-stationary (SnS) properties. Due to the diversity of SnS characteristics across different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase estimation scheme that decouples the problem into multiple subchannel estimation tasks. To solve these sub-tasks, we introduce a novel three-layer Bayesian inference scheme, exploiting the correlations and sparsity of the SnS subchannels in both the spatial and angular domains. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS properties in the spatial domain, and the third layer effectively decouples each subchannel from the observed signal. To enable efficient Bayesian inference, we develop a three-layer generalized approximate message passing (TL-GAMP) algorithm that combines structured variational message passing with belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed TL-GAMP algorithm, demonstrating its robustness across various channel environments, including NF-SnS, NF spatially stationary (NF-SS), and far-field spatially stationary (FF-SS) scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"356-371"},"PeriodicalIF":4.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1109/TSP.2024.3507787
Jian Yi Chen;Yu Zhang;Bing Zhao Li
This paper proposes a graph linear canonical transform (GLCT) by decomposing the linear canonical parameter matrix into fractional Fourier transform, scale transform, and chirp modulation for graph signal processing. The GLCT enables adjustable smoothing modes, enhancing alignment with graph signals. Leveraging traditional fractional domain time-frequency analysis, we investigate vertex-frequency analysis in the graph linear canonical domain, aiming to overcome limitations in capturing local information. Filter design methods, including optimal design and learning with stochastic gradient descent, are analyzed and applied to image classification tasks. The proposed GLCT and vertex-frequency analysis present innovative approaches to signal processing challenges, with potential applications in various fields.
{"title":"Graph Linear Canonical Transform: Definition, Vertex-Frequency Analysis and Filter Design","authors":"Jian Yi Chen;Yu Zhang;Bing Zhao Li","doi":"10.1109/TSP.2024.3507787","DOIUrl":"10.1109/TSP.2024.3507787","url":null,"abstract":"This paper proposes a graph linear canonical transform (GLCT) by decomposing the linear canonical parameter matrix into fractional Fourier transform, scale transform, and chirp modulation for graph signal processing. The GLCT enables adjustable smoothing modes, enhancing alignment with graph signals. Leveraging traditional fractional domain time-frequency analysis, we investigate vertex-frequency analysis in the graph linear canonical domain, aiming to overcome limitations in capturing local information. Filter design methods, including optimal design and learning with stochastic gradient descent, are analyzed and applied to image classification tasks. The proposed GLCT and vertex-frequency analysis present innovative approaches to signal processing challenges, with potential applications in various fields.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5691-5707"},"PeriodicalIF":4.6,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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