IEEE Transactions on Signal Processing最新文献_第7页

Radar Space-Time Domino-Sparse-Pulse Feedback Beampattern Synthesis 雷达时空多米诺稀疏脉冲反馈波束图合成

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-16 DOI: 10.1109/TSP.2025.3560234

Yifan Wu;Junli Liang;Hing Cheung So;Guiwei Liu;Shengqi Zhu;Mingsai Huan

{"title":"Radar Space-Time Domino-Sparse-Pulse Feedback Beampattern Synthesis","authors":"Yifan Wu;Junli Liang;Hing Cheung So;Guiwei Liu;Shengqi Zhu;Mingsai Huan","doi":"10.1109/TSP.2025.3560234","DOIUrl":"10.1109/TSP.2025.3560234","url":null,"abstract":"In radar space-time adaptive processing (STAP), the sliding window size of the filter is a crucial design parameter that significantly influences system performance. A smaller sliding window size while maintaining performance provides several benefits, particularly in terms of faster response and lower range migration probability on the receiver side. In this paper, we tackle the problem of space-time feedback beampattern synthesis (FBS), which reduces the sliding window size by reducing the required number of pulses while maintaining performance. This is achieved through the formulation of two novel models. Motivated by the domino effect, which creates a chain reaction of falling dominoes when the first one is knocked down, the first formulation introduces a novel domino-group-sparsity (DGS) scheme to achieve domino-sparse-pulses (DSP), and its adaptive version is also provided. While the second formulation minimizes the ratio of maximal sidelobe level to the minimal mainlobe level in the FBS to attain performance comparable to the former but with even fewer pulses. Especially, for the latter, we solve the resultant nested-fractional program by decoupling the fractions layer by layer. Simulation results are provided to demonstrate the effectiveness of the proposed methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2053-2069"},"PeriodicalIF":4.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841478","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

DMRA: An Adaptive Line Spectrum Estimation Method Through Dynamical Multiresolution of Atoms 基于原子动态多分辨的自适应线谱估计方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-11 DOI: 10.1109/TSP.2025.3559782

Mingguang Han;Yi Zeng;Xiaoguang Li;Tiejun Li

引用次数: 0

Robust Bayesian Optimization via Localized Online Conformal Prediction 基于局部在线保形预测的鲁棒贝叶斯优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-11 DOI: 10.1109/TSP.2025.3559568

Dongwon Kim;Matteo Zecchin;Sangwoo Park;Joonhyuk Kang;Osvaldo Simeone

{"title":"Robust Bayesian Optimization via Localized Online Conformal Prediction","authors":"Dongwon Kim;Matteo Zecchin;Sangwoo Park;Joonhyuk Kang;Osvaldo Simeone","doi":"10.1109/TSP.2025.3559568","DOIUrl":"10.1109/TSP.2025.3559568","url":null,"abstract":"Bayesian optimization (BO) is a sequential approach for optimizing black-box objective functions using zeroth-order noisy observations. In BO, Gaussian processes (GPs) are employed as probabilistic surrogate models to estimate the objective function based on past observations, guiding the selection of future queries to maximize utility. However, the performance of BO heavily relies on the quality of these probabilistic estimates, which can deteriorate significantly under model misspecification. To address this issue, we introduce localized online conformal prediction-based Bayesian optimization (LOCBO), a BO algorithm that calibrates the GP model through localized online conformal prediction (CP). LOCBO corrects the GP likelihood based on predictive sets produced by LOCBO, and the corrected GP likelihood is then denoised to obtain a calibrated posterior distribution on the objective function. The likelihood calibration step leverages an input-dependent calibration threshold to tailor coverage guarantees to different regions of the input space. Under minimal noise assumptions, we provide theoretical performance guarantees for LOCBO’s iterates that hold for the unobserved objective function. These theoretical findings are validated through experiments on synthetic and real-world optimization tasks, demonstrating that LOCBO consistently outperforms state-of-the-art BO algorithms in the presence of model misspecification.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2039-2052"},"PeriodicalIF":4.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822751","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

Corrections to “Theoretical Bounds in Decentralized Hypothesis Testing” 对“分散假设检验的理论界限”的修正

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-09 DOI: 10.1109/TSP.2025.3553827

Gökhan Gül

引用次数: 0

Randomized Iterative Algorithms for Distributed Massive MIMO Detection 分布式大规模MIMO检测的随机迭代算法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-09 DOI: 10.1109/TSP.2025.3558930

Zheng Wang;Cunhua Pan;Yongming Huang;Shi Jin;Giuseppe Caire

{"title":"Randomized Iterative Algorithms for Distributed Massive MIMO Detection","authors":"Zheng Wang;Cunhua Pan;Yongming Huang;Shi Jin;Giuseppe Caire","doi":"10.1109/TSP.2025.3558930","DOIUrl":"10.1109/TSP.2025.3558930","url":null,"abstract":"Distributed detection over decentralized baseband architectures has emerged as an important problem in the uplink massive MIMO systems. In this paper, the classic Kaczmarz method is fully investigated to facilitate the distributed detection for massive MIMO. First of all, a more general iteration performance result about the traditional randomized block Kaczmarz (RBK) method is derived, which paves the way for conceiving the conditional randomized block Kaczmarz (CRBK) algorithm. By customizing RBK with the concept of conditional sampling, CRBK achieves faster convergence and smaller error bound than RBK. To further exploit the potential of conditional sampling, multi-step conditional randomized block Kaczmarz (MCRBK) algorithm is proposed, which can be readily adopted as a flexible, scalable, low-complexity distributed detection scheme to suit various decentralized baseband architectures in massive MIMO. Moreover, to eliminate the convergence error bound of MCRBK, a novel dynamic step-size mechanism is proposed for the iteration update of MCRBK. Theoretical demonstration shows that the proposed distributed MCRBK detection with the optimized dynamic step-size not only converges exponentially to the solution of linear detection schemes but also enjoys the global convergence to well suit different practical scenarios of massive MIMO.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2304-2319"},"PeriodicalIF":4.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813786","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

A Low-Complexity Chinese Remainder Theorem Based Multi-Carrier Delay Estimation Approach 基于中国剩余定理的低复杂度多载波时延估计方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-08 DOI: 10.1109/TSP.2025.3557851

Yuxiao Zhang;Shuai Wang;Xuanhe Yang;Jiahao Zhang;Gaofeng Pan;Jianping An;Dusit Niyato

{"title":"A Low-Complexity Chinese Remainder Theorem Based Multi-Carrier Delay Estimation Approach","authors":"Yuxiao Zhang;Shuai Wang;Xuanhe Yang;Jiahao Zhang;Gaofeng Pan;Jianping An;Dusit Niyato","doi":"10.1109/TSP.2025.3557851","DOIUrl":"10.1109/TSP.2025.3557851","url":null,"abstract":"With the rapid development of cluster systems, such as unmanned aerial vehicle (UAV) networks and satellite constellations, multi-node collaboration has emerged as a critical requirement. Accurate time synchronization, a cornerstone of such collaborative systems, heavily relies on high-precision delay estimation. Traditional multi-carrier delay estimation methods face an inherent trade-off between estimation accuracy and unambiguous range. While the Chinese Remainder Theorem (CRT)-based approach resolves this dilemma by enabling high-precision estimation without sacrificing range, its computational complexity remains prohibitively high for practical implementations. To address this challenge, we propose a novel low-complexity CRT algorithm based on remainder reconstruction (RR-CRT). By introducing an auxiliary phase to reconstruct erroneous remainders, our method reduces the computational complexity from <inline-formula><tex-math>$O(K^{2})$</tex-math></inline-formula> to <inline-formula><tex-math>$O(K)$</tex-math></inline-formula>, where K denotes the number of subcarriers. Crucially, this reduction in complexity only marginally impacts the algorithm's performance, including the phase error tolerance range, the probability of correctly solving phase ambiguity, and the root mean square error (RMSE) of delay estimation. Numerical simulations validate the effectiveness and robustness of the proposed algorithm, demonstrating its superiority in balancing computational efficiency and estimation performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1643-1657"},"PeriodicalIF":4.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805765","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

Intelligent Reflecting Surface-Assisted Adaptive Beamforming for Blind Interference Suppression 用于盲干扰抑制的智能反射面辅助自适应波束形成

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-08 DOI: 10.1109/TSP.2025.3558965

Peilan Wang;Jun Fang;Bin Wang;Hongbin Li

{"title":"Intelligent Reflecting Surface-Assisted Adaptive Beamforming for Blind Interference Suppression","authors":"Peilan Wang;Jun Fang;Bin Wang;Hongbin Li","doi":"10.1109/TSP.2025.3558965","DOIUrl":"10.1109/TSP.2025.3558965","url":null,"abstract":"In this paper, we consider the problem of adaptive beamforming (ABF) for intelligent reflecting surface (IRS)-assisted systems, where a single antenna receiver, aided by a close-by IRS, tries to decode signals from a legitimate transmitter in the presence of multiple unknown interference signals. Such a problem is formulated as an ABF problem with the objective of minimizing the average received signal power subject to certain constraints. Unlike canonical ABF in array signal processing, we do not have direct access to the covariance matrix that is needed for solving the ABF problem. Instead, for our problem, we only have some quadratic compressive measurements of the covariance matrix. To address this challenge, we propose a sample-efficient method that directly solves the ABF problem without explicitly inferring the covariance matrix. Compared with the methods which explicitly recover the covariance matrix from its quadratic compressive measurements, our proposed method achieves a substantial improvement in terms of sample efficiency. Simulation results show that our method, using a small number of measurements, can effectively nullify the interference signals and enhance the signal-to-interference-plus-noise ratio (SINR).","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1744-1758"},"PeriodicalIF":4.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805764","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

A Universal Low-Dimensional Subspace Structure in Beamforming Design: Theory and Applications 波束形成设计中一种通用的低维子空间结构：理论与应用

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-03 DOI: 10.1109/TSP.2025.3557523

Xiaotong Zhao;Qingjiang Shi

{"title":"A Universal Low-Dimensional Subspace Structure in Beamforming Design: Theory and Applications","authors":"Xiaotong Zhao;Qingjiang Shi","doi":"10.1109/TSP.2025.3557523","DOIUrl":"10.1109/TSP.2025.3557523","url":null,"abstract":"Beamforming design plays a crucial role in multi-antenna systems, with numerous methods proposed to optimize key performance metrics such as spectral efficiency and power consumption. However, these methods often face two major challenges: high computational complexity and excessive communication overhead in distributed implementations. This paper addresses these challenges by analyzing a general beamforming optimization framework—referred to as the standard-form beamforming problem—which encompasses various beamforming design tasks. We prove that any positive stationary point of this problem exhibits a low-dimensional subspace (LDS) structure, enabling the development of low-complexity and communication-efficient beamforming algorithms. As an illustrative example, we leverage the LDS structure to propose a computationally efficient beamforming algorithm for weighted sum rate maximization in coordinated multi-cell systems, with provable convergence to stationary points. Furthermore, we decentralize the algorithm for distributed coordinated beamforming, ensuring low interaction costs independent of the number of base station antennas. Notably, the proposed LDS structure is broadly applicable to a wide range of beamforming problems, including integrated sensing and communication (ISAC), intelligent reflecting surfaces (IRS), and beyond. Extensive numerical simulations validate the effectiveness and versatility of our approach, particularly the general applicability of the LDS structure.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1775-1791"},"PeriodicalIF":4.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775440","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

Inverse Particle Filter 逆粒子滤波

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-04-01 DOI: 10.1109/TSP.2025.3556702

Himali Singh;Arpan Chattopadhyay;Kumar Vijay Mishra

{"title":"Inverse Particle Filter","authors":"Himali Singh;Arpan Chattopadhyay;Kumar Vijay Mishra","doi":"10.1109/TSP.2025.3556702","DOIUrl":"10.1109/TSP.2025.3556702","url":null,"abstract":"In cognitive systems, recent emphasis has been placed on studying the cognitive processes of the subject whose behavior was the primary focus of the system’s cognitive response. This approach, known as <italic>inverse cognition</i>, arises in counter-adversarial applications and has motivated the development of inverse Bayesian filters. In this context, a cognitive adversary, such as a radar, uses a forward Bayesian filter to track its target of interest. An inverse filter is then employed to infer the adversary’s estimate of the target’s or defender’s state. Previous studies have addressed this inverse filtering problem by introducing methods like the inverse Kalman filter (KF), inverse extended KF, and inverse unscented KF. However, these filters typically assume additive Gaussian noise models and/or rely on local approximations of non-linear dynamics at the state estimates, limiting their practical application. In contrast, this paper adopts a global filtering approach and presents the development of an inverse particle filter (I-PF). The particle filter framework employs Monte Carlo methods to approximate arbitrary posterior distributions. Moreover, under mild system-level conditions, the proposed I-PF demonstrates convergence to the optimal inverse filter. Additionally, we propose the differentiable I-PF to address scenarios where system information is unknown to the defender. Using the recursive Cramér-Rao lower bound and non-credibility index, our numerical experiments for different systems demonstrate the estimation performance and time complexity of the proposed filter.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1922-1938"},"PeriodicalIF":4.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757835","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

A Bayesian Mixture Model of Temporal Point Processes With Determinantal Point Process Prior 具有确定性点过程先验的时间点过程贝叶斯混合模型

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-30 DOI: 10.1109/TSP.2025.3575175

Yiwei Dong;Shaoxin Ye;Qiyu Han;Yuwen Cao;Hongteng Xu;Hanfang Yang

引用次数: 0