Pub Date : 2025-12-17DOI: 10.1109/TSP.2025.3645629
Zhen Qin;Zhihui Zhu
As intelligent reflecting surface (IRS) has emerged as a new and promising technology capable of configuring the wireless environment favorably, channel estimation for IRS-assisted multiple-input multiple-output (MIMO) systems has garnered extensive attention in recent years. Despite the development of numerous algorithms to address this challenge, a comprehensive theoretical characterization of the optimal recovery error is still lacking. This paper aims to address this gap by providing theoretical guarantees in terms of stable recovery of channel matrices for noisy measurements. We begin by establishing the equivalence between IRS-assisted MIMO systems in the uplink scenario and a compact tensor train (TT)-based tensor-on-tensor (ToT) regression. Building on this equivalence, we then investigate the restricted isometry property (RIP) for complex-valued subgaussian measurements. Our analysis reveals that successful recovery hinges on the relationship between the number of user terminals and the number of time slots during which channel matrices remain invariant. Utilizing the RIP condition, we establish a theoretical upper bound on the recovery error for solutions to the constrained least-squares optimization problem, as well as a minimax lower bound for the considered model. Our analysis demonstrates that the recovery error decreases inversely with the number of time slots, and increases proportionally with the total number of unknown entries in the channel matrices, thereby quantifying the fundamental trade-offs in channel estimation accuracy. In addition, we explore a multi-hop IRS scheme and analyze the corresponding recovery errors. Finally, we have performed numerical experiments to support our theoretical findings.
{"title":"Optimal Error Analysis of Channel Estimation for IRS-Assisted MIMO Systems","authors":"Zhen Qin;Zhihui Zhu","doi":"10.1109/TSP.2025.3645629","DOIUrl":"10.1109/TSP.2025.3645629","url":null,"abstract":"As intelligent reflecting surface (IRS) has emerged as a new and promising technology capable of configuring the wireless environment favorably, channel estimation for IRS-assisted multiple-input multiple-output (MIMO) systems has garnered extensive attention in recent years. Despite the development of numerous algorithms to address this challenge, a comprehensive theoretical characterization of the optimal recovery error is still lacking. This paper aims to address this gap by providing theoretical guarantees in terms of stable recovery of channel matrices for noisy measurements. We begin by establishing the equivalence between IRS-assisted MIMO systems in the uplink scenario and a compact tensor train (TT)-based tensor-on-tensor (ToT) regression. Building on this equivalence, we then investigate the restricted isometry property (RIP) for complex-valued subgaussian measurements. Our analysis reveals that successful recovery hinges on the relationship between the number of user terminals and the number of time slots during which channel matrices remain invariant. Utilizing the RIP condition, we establish a theoretical upper bound on the recovery error for solutions to the constrained least-squares optimization problem, as well as a minimax lower bound for the considered model. Our analysis demonstrates that the recovery error decreases inversely with the number of time slots, and increases proportionally with the total number of unknown entries in the channel matrices, thereby quantifying the fundamental trade-offs in channel estimation accuracy. In addition, we explore a multi-hop IRS scheme and analyze the corresponding recovery errors. Finally, we have performed numerical experiments to support our theoretical findings.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"61-74"},"PeriodicalIF":5.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770889","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 : 2025-12-17DOI: 10.1109/TSP.2025.3644869
Ziqing Lu;Guanlin Liu;Lifeng Lai;Weiyu Xu
The multiple agent reinforcement learning systems (MARL) based on the Markov Game (MG) have emerged in many critical applications. To improve the robustness/defense of MARL systems against adversaries, studying various adversarial attacks on reinforcement learning systems is very important. Previous works on adversarial attacks considered some possible features to attack in the MG, such as action poisoning attacks, reward poisoning attacks, and state perception attacks. In this paper, we propose a new form of perception attack, called the camouflage attack in MARL systems. In the camouflage attack, the attackers change the appearances of some objects in the environment but without changing the actual objects; and the camouflaged appearances may look the same to all the targeted recipient (victim) agents. The camouflaged appearances can mislead the recipient agents to follow misguided policies. We evaluate the effect of camouflage attacks in two different scenarios: Camouflage attacks were performed during the learning (training-time attacks) and were performed during the test of agents’ policies (test-time attacks). Our numerical and theoretical results show that camouflage attacks can rival the more conventional, but likely more difficult state perception attacks, by comparing their effect on reducing agents’ global benefits. We also investigated cost-constrained camouflage attacks, compared them with cost-constrained state perception attacks, and showed how cost budgets affect attack performance numerically.
{"title":"Camouflage Adversarial Attacks on Multi-Agent Reinforcement Learning Systems","authors":"Ziqing Lu;Guanlin Liu;Lifeng Lai;Weiyu Xu","doi":"10.1109/TSP.2025.3644869","DOIUrl":"10.1109/TSP.2025.3644869","url":null,"abstract":"The multiple agent reinforcement learning systems (MARL) based on the Markov Game (MG) have emerged in many critical applications. To improve the robustness/defense of MARL systems against adversaries, studying various adversarial attacks on reinforcement learning systems is very important. Previous works on adversarial attacks considered some possible features to attack in the MG, such as action poisoning attacks, reward poisoning attacks, and state perception attacks. In this paper, we propose a new form of perception attack, called the camouflage attack in MARL systems. In the camouflage attack, the attackers change the appearances of some objects in the environment but without changing the actual objects; and the camouflaged appearances may look the same to all the targeted recipient (victim) agents. The camouflaged appearances can mislead the recipient agents to follow misguided policies. We evaluate the effect of camouflage attacks in two different scenarios: Camouflage attacks were performed during the learning (training-time attacks) and were performed during the test of agents’ policies (test-time attacks). Our numerical and theoretical results show that camouflage attacks can rival the more conventional, but likely more difficult state perception attacks, by comparing their effect on reducing agents’ global benefits. We also investigated cost-constrained camouflage attacks, compared them with cost-constrained state perception attacks, and showed how cost budgets affect attack performance numerically.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"589-604"},"PeriodicalIF":5.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770882","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 : 2025-12-17DOI: 10.1109/tsp.2025.3644686
Marco Carpentiero, Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
{"title":"Doubly Adaptive Social Learning","authors":"Marco Carpentiero, Virginia Bordignon, Vincenzo Matta, Ali H. Sayed","doi":"10.1109/tsp.2025.3644686","DOIUrl":"https://doi.org/10.1109/tsp.2025.3644686","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770883","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 : 2025-12-15DOI: 10.1109/tsp.2025.3643595
Anton Björkman, David Sundström, Andreas Jakobsson, Filip Elvander
{"title":"Optimal Transport Regularization for Simulation-Informed Room Impulse Response Estimation","authors":"Anton Björkman, David Sundström, Andreas Jakobsson, Filip Elvander","doi":"10.1109/tsp.2025.3643595","DOIUrl":"https://doi.org/10.1109/tsp.2025.3643595","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759597","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 : 2025-12-12DOI: 10.1109/tsp.2025.3643309
Zhidi Lin, Ying Li, Feng Yin, Juan Maroñas, Alexandre H. Thiéry
{"title":"Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems","authors":"Zhidi Lin, Ying Li, Feng Yin, Juan Maroñas, Alexandre H. Thiéry","doi":"10.1109/tsp.2025.3643309","DOIUrl":"https://doi.org/10.1109/tsp.2025.3643309","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"146 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731418","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 : 2025-12-11DOI: 10.1109/TSP.2025.3642042
Adarsh M. Subramaniam;Argyrios Gerogiannis;James Z. Hare;Venugopal V. Veeravalli
State of the art methods for target tracking with sensor management (or controlled sensing) are model-based and are obtained through solutions to Partially Observable Markov Decision Process (POMDP) formulations. In this paper a Reinforcement Learning (RL) approach to the problem is explored for the setting where the motion model for the object/target to be tracked is unknown to the observer. It is assumed that the target dynamics are stationary in time, the state space and the observation space are discrete, and there is complete observability of the location of the target under certain (a priori unknown) sensor control actions. Then, a novel Markov Decision Process (MDP) rather than POMDP formulation is proposed for the tracking problem with controlled sensing, which is termed as Track-MDP. In contrast to the POMDP formulation, the Track-MDP formulation is amenable to an RL based solution. It is shown that the optimal policy for the Track-MDP formulation, which is approximated through RL, is guaranteed to track all significant target paths with certainty. The Track-MDP method is then compared with the optimal POMDP policy, and it is shown that the infinite horizon tracking reward of the optimal Track-MDP policy is the same as that of the optimal POMDP policy. In simulations it is demonstrated that Track-MDP based RL can lead to a policy that can track the target with high accuracy and superior energy efficiency.
{"title":"Track-MDP: Reinforcement Learning for Target Tracking With Controlled Sensing","authors":"Adarsh M. Subramaniam;Argyrios Gerogiannis;James Z. Hare;Venugopal V. Veeravalli","doi":"10.1109/TSP.2025.3642042","DOIUrl":"10.1109/TSP.2025.3642042","url":null,"abstract":"State of the art methods for target tracking with sensor management (or controlled sensing) are model-based and are obtained through solutions to Partially Observable Markov Decision Process (POMDP) formulations. In this paper a Reinforcement Learning (RL) approach to the problem is explored for the setting where the motion model for the object/target to be tracked is unknown to the observer. It is assumed that the target dynamics are stationary in time, the state space and the observation space are discrete, and there is complete observability of the location of the target under certain (a priori unknown) sensor control actions. Then, a novel Markov Decision Process (MDP) rather than POMDP formulation is proposed for the tracking problem with controlled sensing, which is termed as Track-MDP. In contrast to the POMDP formulation, the Track-MDP formulation is amenable to an RL based solution. It is shown that the optimal policy for the Track-MDP formulation, which is approximated through RL, is guaranteed to track all significant target paths with certainty. The Track-MDP method is then compared with the optimal POMDP policy, and it is shown that the infinite horizon tracking reward of the optimal Track-MDP policy is the same as that of the optimal POMDP policy. In simulations it is demonstrated that Track-MDP based RL can lead to a policy that can track the target with high accuracy and superior energy efficiency.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5348-5361"},"PeriodicalIF":5.8,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728939","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 : 2025-12-10DOI: 10.1109/TSP.2025.3642179
Ruiding Hou;Jiaheng Wang;Rui Zhou;Daniel P. Palomar;Xiqi Gao;Björn Ottersten
Precoding techniques, particularly linear precoding, are widely employed in multiple-input multiple-output (MIMO) systems. Although well-studied in the literature, linear precoding design still faces two fundamental challenges: high computational complexity and the lack of a general design approach. This paper presents an efficient and unified framework for linear precoding design in downlink multiuser systems that accommodates diverse criteria, such as weighted sum rate (WSR) maximization and weighted symbol error rate (WSER) minimization, while ensuring quality of service (QoS) requirements. The proposed framework achieves an order-of-magnitude reduction in per-iteration computational complexity compared to existing methods. In particular, by accurately characterizing the feasible signal-to-interference-plus-noise ratio (SINR) region, we transform the high-dimensional precoding design problem into a more manageable, low-dimensional SINR allocation problem. We propose an efficient SINR-based precoding (SBP) framework that employs a water-filling solution at each iteration, without the need for matrix inversion. The proposed framework can be extended to broadcast and interference channels with multi-antenna users under pre-fixed receivers. Simulation results demonstrate that our method achieves near-optimal performance while significantly reducing computational complexity compared to existing methods, such as the weighted minimum mean square error (WMMSE) method.
{"title":"An Efficient and Unified Framework for Downlink Linear Precoding with QoS Constraints","authors":"Ruiding Hou;Jiaheng Wang;Rui Zhou;Daniel P. Palomar;Xiqi Gao;Björn Ottersten","doi":"10.1109/TSP.2025.3642179","DOIUrl":"10.1109/TSP.2025.3642179","url":null,"abstract":"Precoding techniques, particularly linear precoding, are widely employed in multiple-input multiple-output (MIMO) systems. Although well-studied in the literature, linear precoding design still faces two fundamental challenges: high computational complexity and the lack of a general design approach. This paper presents an efficient and unified framework for linear precoding design in downlink multiuser systems that accommodates diverse criteria, such as weighted sum rate (WSR) maximization and weighted symbol error rate (WSER) minimization, while ensuring quality of service (QoS) requirements. The proposed framework achieves an order-of-magnitude reduction in per-iteration computational complexity compared to existing methods. In particular, by accurately characterizing the feasible signal-to-interference-plus-noise ratio (SINR) region, we transform the high-dimensional precoding design problem into a more manageable, low-dimensional SINR allocation problem. We propose an efficient SINR-based precoding (SBP) framework that employs a water-filling solution at each iteration, without the need for matrix inversion. The proposed framework can be extended to broadcast and interference channels with multi-antenna users under pre-fixed receivers. Simulation results demonstrate that our method achieves near-optimal performance while significantly reducing computational complexity compared to existing methods, such as the weighted minimum mean square error (WMMSE) method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"276-292"},"PeriodicalIF":5.8,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717710","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 : 2025-12-09DOI: 10.1109/TSP.2025.3641941
Shenghua Hu;Guangyang Zeng;Wenchao Xue;Haitao Fang;Junfeng Wu;Biqiang Mu
We study the problem of signal source localization using received signal strength measurements. We begin by presenting verifiable geometric conditions for sensor deployment that ensure the model’s asymptotic localizability. Then we establish the consistency and asymptotic efficiency of the maximum likelihood (ML) estimator. However, computing the ML estimator is challenging due to its reliance on solving a non-convex optimization problem. To overcome this, we propose a two-step estimator that retains the same asymptotic properties as the ML estimator while offering low computational complexity—linear in the number of measurements. The main challenge lies in obtaining a consistent estimator in the first step. To address this, we construct two linear least-squares estimation problems by applying algebraic transformations to the nonlinear measurement model, leading to closed-form solutions. In the second step, we perform a single Gauss-Newton iteration using the consistent estimator from the first step as the initialization, achieving the same asymptotic efficiency as the ML estimator. Finally, simulation results validate the theoretical property and practical effectiveness of the proposed two-step estimator.
{"title":"RSS-Based Localization: Ensuring Consistency and Asymptotic Efficiency","authors":"Shenghua Hu;Guangyang Zeng;Wenchao Xue;Haitao Fang;Junfeng Wu;Biqiang Mu","doi":"10.1109/TSP.2025.3641941","DOIUrl":"10.1109/TSP.2025.3641941","url":null,"abstract":"We study the problem of signal source localization using received signal strength measurements. We begin by presenting verifiable geometric conditions for sensor deployment that ensure the model’s asymptotic localizability. Then we establish the consistency and asymptotic efficiency of the maximum likelihood (ML) estimator. However, computing the ML estimator is challenging due to its reliance on solving a non-convex optimization problem. To overcome this, we propose a two-step estimator that retains the same asymptotic properties as the ML estimator while offering low computational complexity—linear in the number of measurements. The main challenge lies in obtaining a consistent estimator in the first step. To address this, we construct two linear least-squares estimation problems by applying algebraic transformations to the nonlinear measurement model, leading to closed-form solutions. In the second step, we perform a single Gauss-Newton iteration using the consistent estimator from the first step as the initialization, achieving the same asymptotic efficiency as the ML estimator. Finally, simulation results validate the theoretical property and practical effectiveness of the proposed two-step estimator.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5257-5272"},"PeriodicalIF":5.8,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717743","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}
Composite federated learning offers a general framework for solving machine learning problems with additional regularization terms. However, existing methods often face significant limitations: many require clients to perform computationally expensive proximal operations, and their performance is frequently vulnerable to data heterogeneity. To overcome these challenges, we propose a novel composite federated learning algorithm called FedCanon, designed to solve the optimization problems comprising a possibly non-convex loss function and a weakly convex, potentially non-smooth regularization term. By decoupling proximal mappings from local updates, FedCanon requires only a single proximal evaluation on the server per iteration, thereby reducing the overall proximal computation cost. Concurrently, it integrates control variables into local updates to mitigate the client drift arising from data heterogeneity. The entire architecture avoids the complex subproblems of primal-dual alternatives. The theoretical analysis provides the first rigorous convergence guarantees for this proximal-skipping framework in the general non-convex setting. It establishes that FedCanon achieves a sublinear convergence rate, and a linear rate under the Polyak-Łojasiewicz condition, without the restrictive bounded heterogeneity assumption. Extensive experiments demonstrate that FedCanon outperforms the state-of-the-art methods in terms of both accuracy and computational efficiency, particularly under heterogeneous data distributions.
{"title":"FedCanon: Non-Convex Composite Federated Learning With Efficient Proximal Operation on Heterogeneous Data","authors":"Yuan Zhou;Jiachen Zhong;Xinli Shi;Guanghui Wen;Xinghuo Yu","doi":"10.1109/TSP.2025.3642025","DOIUrl":"10.1109/TSP.2025.3642025","url":null,"abstract":"Composite federated learning offers a general framework for solving machine learning problems with additional regularization terms. However, existing methods often face significant limitations: many require clients to perform computationally expensive proximal operations, and their performance is frequently vulnerable to data heterogeneity. To overcome these challenges, we propose a novel composite federated learning algorithm called FedCanon, designed to solve the optimization problems comprising a possibly non-convex loss function and a weakly convex, potentially non-smooth regularization term. By decoupling proximal mappings from local updates, FedCanon requires only a single proximal evaluation on the server per iteration, thereby reducing the overall proximal computation cost. Concurrently, it integrates control variables into local updates to mitigate the client drift arising from data heterogeneity. The entire architecture avoids the complex subproblems of primal-dual alternatives. The theoretical analysis provides the first rigorous convergence guarantees for this proximal-skipping framework in the general non-convex setting. It establishes that FedCanon achieves a sublinear convergence rate, and a linear rate under the Polyak-Łojasiewicz condition, without the restrictive bounded heterogeneity assumption. Extensive experiments demonstrate that FedCanon outperforms the state-of-the-art methods in terms of both accuracy and computational efficiency, particularly under heterogeneous data distributions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"215-229"},"PeriodicalIF":5.8,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717742","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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