IEEE Transactions on Signal Processing最新文献_第4页

Efficient Off-Grid Near-Field Cascade Channel Estimation for XL-IRS Systems via Tucker Decomposition 基于Tucker分解的xml - irs系统离网近场级联信道估计

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-02-17 DOI: 10.1109/tsp.2026.3665590

Wenzhou Cao, Yashuai Cao, Tiejun Lv, Mugen Peng

引用次数: 0

Nonlinear Sparse Bayesian Learning Methods With Application to Massive MIMO Channel Estimation With Hardware Impairments 非线性稀疏贝叶斯学习方法在具有硬件缺陷的海量MIMO信道估计中的应用

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-02-13 DOI: 10.1109/TSP.2026.3664263

Arttu Arjas;Italo Atzeni

{"title":"Nonlinear Sparse Bayesian Learning Methods With Application to Massive MIMO Channel Estimation With Hardware Impairments","authors":"Arttu Arjas;Italo Atzeni","doi":"10.1109/TSP.2026.3664263","DOIUrl":"10.1109/TSP.2026.3664263","url":null,"abstract":"Accurate channel estimation is critical for realizing the performance gains of massive multiple-input multiple-output (MIMO) systems. Traditional approaches to channel estimation typically assume ideal receiver hardware and linear signal models. However, practical receivers suffer from impairments such as nonlinearities in the low-noise amplifiers and quantization errors, which invalidate standard model assumptions and degrade the estimation accuracy. In this work, we propose a nonlinear channel estimation framework that models the distortion function arising from hardware impairments using Gaussian process (GP) regression while leveraging the inherent sparsity of massive MIMO channels. First, we form a GP-based surrogate of the distortion function, employing pseudo-inputs to reduce the computational complexity. Then, we integrate the GP-based surrogate of the distortion function into newly developed enhanced sparse Bayesian learning (SBL) methods, enabling distortion-aware sparse channel estimation. Specifically, we propose two nonlinear SBL methods based on distinct optimization objectives, each offering a different trade-off between estimation accuracy and computational complexity. Numerical results demonstrate significant gains over the Bussgang linear minimum mean squared error estimator and linear SBL, particularly under strong distortion and at high signal-to-noise ratio.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"830-845"},"PeriodicalIF":5.8,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146198499","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}

引用次数: 0

Countering Collaborative Eavesdroppers Under Imperfect CSI: Outage Probability Constraint Transformation and Zeroth-Order Optimization 不完全CSI下的协同窃听：中断概率约束变换与零阶优化

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-02-06 DOI: 10.1109/TSP.2026.3661988

Hancheng Zhu;Zongze Li;Yik-Chung Wu;H. Vincent Poor

{"title":"Countering Collaborative Eavesdroppers Under Imperfect CSI: Outage Probability Constraint Transformation and Zeroth-Order Optimization","authors":"Hancheng Zhu;Zongze Li;Yik-Chung Wu;H. Vincent Poor","doi":"10.1109/TSP.2026.3661988","DOIUrl":"10.1109/TSP.2026.3661988","url":null,"abstract":"This paper addresses the challenge of secure transmission in wireless networks in the presence of intelligent collaborative eavesdroppers and when the knowledge of eavesdroppers’ channel state information (CSI) is imperfect. The secure outage probability (SOP) constraint, which involves a complex summation of signal-to-interference-plus-noise ratios (SINRs), is transformed into a deterministic form using an asymptotic expression. However, the deterministic SOP expression still poses challenges in optimization. To this end, a penalty-based stochastic zeroth-order (ZO) optimization method is proposed. Additionally, an accelerated algorithm combining ZO and manifold optimization is developed to enhance convergence and ensure solution quality. The proposed algorithms are applicable to scenarios with or without artificial noise (AN) and to both single-cell or cell-free systems, enabling multiple scenarios to be handled under a general framework. Simulation results demonstrate that the proposed methods outperform existing algorithms across various network settings. The study also reveals that cell-free systems offer superior security compared to single-cell systems, highlighting the importance of spatial diversity in countering collaborative eavesdroppers.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"863-877"},"PeriodicalIF":5.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134873","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}

引用次数: 0

Stable Representation Learning via Generalized Learnable Graph Scattering Transform 基于广义可学习图散射变换的稳定表示学习

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-02-04 DOI: 10.1109/TSP.2026.3660885

Shiyu Liu;Wenrui Dai;Shaohui Li;Ziyang Zheng;Junni Zou;Hongkai Xiong

{"title":"Stable Representation Learning via Generalized Learnable Graph Scattering Transform","authors":"Shiyu Liu;Wenrui Dai;Shaohui Li;Ziyang Zheng;Junni Zou;Hongkai Xiong","doi":"10.1109/TSP.2026.3660885","DOIUrl":"10.1109/TSP.2026.3660885","url":null,"abstract":"Graph scattering networks (GSNs) are typically non-trainable graph convolutional networks that realize multi-layer representation learning via analytic graph wavelets with theoretical stability guarantees but are restricted in the flexibility and capacity of representation learning. Recent attempts on trainable GSNs introduce learnable parameters for graph wavelets but are still limited to wavelet families and the modulus nonlinearity. To address this problem, in this paper, we propose the first generalized learnable graph scattering transform (GST) that achieves stable representation learning using a general class of adaptive bandpass graph filters with Lipschitz continuous nonlinearities. The proposed GST learns the bandpass graph filters from arbitrary nonnegative Lipschitz continuous functions, including graph wavelet families and polynomial functions. We demonstrate in theory that learnable graph scattering networks (GSNs) constructed with the proposed GST ensures the upper bounds of difference in learned representations are solely related to the perturbation on graph signal and topology. Furthermore, the proposed GST is generalized to Lipschitz continuous nonlinearites like hyperbolic tangent and ReLU functions beyond the modulus operation with theoretical stability guarantees. Experimental results show that the proposed GSNs evidently outperform existing graph scattering networks in graph and node classification tasks and achieve state-of-the-art performance in node classification by learning multi-scale bandpass representations.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"701-716"},"PeriodicalIF":5.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115885","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}

引用次数: 0

Distributed Penalty Nesterov Gradient Algorithm For Extremely Large-Scale MIMO Systems 超大规模MIMO系统的分布式惩罚Nesterov梯度算法

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-02-03 DOI: 10.1109/TSP.2026.3660267

Qiqiang Chen;Zheng Wang;Chuan Zhang;Feng Shu;Yongming Huang;Dusit Niyato

{"title":"Distributed Penalty Nesterov Gradient Algorithm For Extremely Large-Scale MIMO Systems","authors":"Qiqiang Chen;Zheng Wang;Chuan Zhang;Feng Shu;Yongming Huang;Dusit Niyato","doi":"10.1109/TSP.2026.3660267","DOIUrl":"10.1109/TSP.2026.3660267","url":null,"abstract":"In this paper, we propose the distributed penalty Nesterov gradient (DPNG) detection algorithm for uplink extremely large-scale multiple-input multiple-output (XL-MIMO) systems, to address the performance limitations of conventional linear detectors. Instead of directly solving the maximum likelihood (ML) detection problem, we introduce a convex relaxation by incorporating a tailored penalty function that enforces alignment between the estimates and the constellation set. By integrating the diffusion strategy into reformulated detection problem, the DPNG algorithm enables efficient distributed computation. According to theoretical analysis, we prove this reformulated detection problem to be strongly convex and Lipschitz continuous. Leveraging these properties, the optimal solution of the reformulated problem achieves better performance than traditional linear detection. Furthermore, theoretical result demonstrates that the proposed DPNG algorithm efficiently finds this solution with a sublinear convergence rate, precisely quantifying the trade-off between detection performance and computational complexity. Extensive simulations confirm that DPNG achieves notable BER improvements with lower complexity and bandwidth, thereby providing practical advantages over prior methods in distributed XL-MIMO detection.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"734-749"},"PeriodicalIF":5.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110923","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}

引用次数: 0

Warped Time Series Anomaly Detection 扭曲时间序列异常检测

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-30 DOI: 10.1109/TSP.2026.3657450

Charlotte Lacoquelle;Xavier Pucel;Louise Travé Massuyès;Axel Reymonet;Benoît Enaux

引用次数: 0

T-Rex: Fitting a Robust Factor Model via Expectation-Maximization 霸王龙：通过期望最大化拟合稳健因子模型

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-30 DOI: 10.1109/TSP.2026.3659721

Daniel Cederberg

引用次数: 0

Robust State Estimation for Time-Difference-of-Arrival Localization Systems Under Measurement Loss 测量损失下到达时差定位系统的鲁棒状态估计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-30 DOI: 10.1109/TSP.2026.3658065

Yuhao Cui;Huabo Liu;Keke Huang;Yao Mao;Haisheng Yu

{"title":"Robust State Estimation for Time-Difference-of-Arrival Localization Systems Under Measurement Loss","authors":"Yuhao Cui;Huabo Liu;Keke Huang;Yao Mao;Haisheng Yu","doi":"10.1109/TSP.2026.3658065","DOIUrl":"10.1109/TSP.2026.3658065","url":null,"abstract":"In this paper, we investigate the problem of robust state estimation in time-difference-of-arrival (TDOA) localization systems under scenarios involving random measurement loss. Traditional TDOA localization methods typically rely on the extended Kalman filter (EKF), which performs local linearization of the nonlinear system model using a first-order Taylor expansion. However, the EKF relies on a first-order linearization of the system model, which inevitably introduces approximation errors and compromises the accuracy of state estimation. In addition, when the fusion center is subject to Denial-of-Service (DoS) attacks, communication interruptions, or data tampering, measurement data may be lost or corrupted, further compromising the stability and reliability of the state estimation process. To address these challenges, we augment the EKF cost function with a penalty term on the innovation sensitivity to modeling errors, thereby proposing a robust state estimation algorithm with enhanced resilience to disturbances and system uncertainties. Under certain assumptions, it is theoretically established that the proposed robust estimator guarantees bounded estimation error. Numerical simulations indicate that, in three-dimensional TDOA-based localization scenarios, the proposed algorithm achieves improved estimation accuracy compared to the standard EKF.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"471-482"},"PeriodicalIF":5.8,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089995","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}

引用次数: 0

Modeling and Statistical Characterization of Large-Scale Automotive Radar Networks 大型汽车雷达网络的建模与统计特性

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-28 DOI: 10.1109/TSP.2026.3659022

Mohammad Taha Shah;Gourab Ghatak;Ankit Kumar;Shobha Sundar Ram

{"title":"Modeling and Statistical Characterization of Large-Scale Automotive Radar Networks","authors":"Mohammad Taha Shah;Gourab Ghatak;Ankit Kumar;Shobha Sundar Ram","doi":"10.1109/TSP.2026.3659022","DOIUrl":"10.1109/TSP.2026.3659022","url":null,"abstract":"The impact of discrete clutter and co-channel interference on the performance of automotive radar networks has been studied using stochastic geometry, in particular, by leveraging Poisson point processes (PPPs). However, such characterization does not take into account the impact of street geometry and the fact that the location of the automotive radars are restricted to the streets as their domain rather than the entire Euclidean plane. In addition, the structure of the streets may change drastically as a vehicle moves out of a city center towards the suburban areas. Consequently, not only the radar performance change but also the radar parameters and protocols must be adapted for optimum performance. In this paper, we propose and characterize line and Cox process-based street and point models to analyze large-scale automotive radar networks. We consider the classical Poisson line process (PLP) and the newly introduced Binomial line process (BLP) model to emulate the streets, and the corresponding PPP-based Cox process to emulate the vehicular nodes. In particular, the BLP model effectively considers the spatial variation of street geometry across different parts of the city. We derive the effective interference set experienced by an automotive radar, the statistics of distance to interferers, and characterize the detection probability of the ego radar as a function of street and vehicle density. Finally, leveraging the real-world data on urban streets and vehicle density across different cities of the world, we present how the radar performance varies in different parts of the city as well as across different times of the day. Thus, our study equips network operators and automotive manufacturers with system design insights to optimize automotive radar networks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"717-733"},"PeriodicalIF":5.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070288","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}

引用次数: 0

Treating the Filter Weights as Learnable Functions: An Efficient Nonlinear Filtering Framework and Its Adaptive Algorithms 将滤波器权值视为可学习函数：一种有效的非线性滤波框架及其自适应算法

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-26 DOI: 10.1109/TSP.2026.3657751

Mingjing Cui;Dongyuan Lin;Lei Li;Yunfei Zheng;Shiyuan Wang

{"title":"Treating the Filter Weights as Learnable Functions: An Efficient Nonlinear Filtering Framework and Its Adaptive Algorithms","authors":"Mingjing Cui;Dongyuan Lin;Lei Li;Yunfei Zheng;Shiyuan Wang","doi":"10.1109/TSP.2026.3657751","DOIUrl":"10.1109/TSP.2026.3657751","url":null,"abstract":"Adaptive filters, constrained by a linear filtering framework, often struggle with nonlinear modeling in complex processes. Kernel adaptive filters (KAFs) offer a promising solution by mapping input signals into diverse feature spaces. However, their computational efficiency and filtering accuracy may still not meet the demands of practical applications. To this end, based on Kolmogorov-Arnold (KA) representation theorem, this paper proposes a novel nonlinear filtering framework by treating filter weights as learnable functions. Specifically, the learnable functions are represented as a linear combination of multiple Gaussian basis functions with different centers. To determine the coefficients that define these learnable functions, the weight-learning-based least mean square (WL-LMS) and weight-learning-based recursive least squares (WL-RLS) algorithms are further proposed based on minimum mean square error (MMSE). In addition, to ensure convergence and assess the steady-state and transient performance of proposed algorithms, a thorough theoretical analysis of the convergence conditions and excess mean square error (EMSE) is provided. Finally, linear-in-parameters system identifications validate the correctness of theoretical analysis, while chaotic time-series prediction and nonlinear system identifications demonstrate the superiorities of the proposed WL-LMS and WL-RLS algorithms.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"545-560"},"PeriodicalIF":5.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056290","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}

引用次数: 0