IEEE Transactions on Signal Processing最新文献_第6页

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

A Construction of Pairwise Co-Prime Integer Matrices of Any Dimension and Their Least Common Right Multiple 任意维成对协素数整数矩阵及其最小公右倍数的构造

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-26 DOI: 10.1109/TSP.2025.3572819

Guangpu Guo;Xiang-Gen Xia

{"title":"A Construction of Pairwise Co-Prime Integer Matrices of Any Dimension and Their Least Common Right Multiple","authors":"Guangpu Guo;Xiang-Gen Xia","doi":"10.1109/TSP.2025.3572819","DOIUrl":"10.1109/TSP.2025.3572819","url":null,"abstract":"Compared with co-prime integers, co-prime integer matrices are more challenging due to the non-commutativity. In this paper, we present a new family of pairwise co-prime integer matrices of any dimension and large size. These matrices are non-commutative and have low spread, i.e., their ratios of peak absolute values to mean absolute values (or the smallest non-zero absolute values) of their components are low. When matrix dimension is larger than 2, this family of matrices differs from the existing families, such as circulant, Toeplitz matrices, or triangular matrices, and therefore, offers more varieties in applications. In this paper, we first prove the pairwise coprimality of the constructed matrices, then determine their determinant absolute values, and their least common right multiple (lcrm) with a closed and simple form. We also analyze their sampling rates when these matrices are used as sampling matrices for a multi-dimensional signal. The proposed family of pairwise co-prime integer matrices may have applications in multi-dimensional Chinese remainder theorem (MD-CRT) that can be used to determine integer vectors from their integer vector remainders modulo a set of integer matrix moduli, and also in multi-dimensional sparse sensing and multirate systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2187-2199"},"PeriodicalIF":4.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146073","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

Performance Analysis and Power Allocation for Massive MIMO ISAC Systems 大规模MIMO ISAC系统的性能分析与功率分配

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-26 DOI: 10.1109/TSP.2025.3554012

Nhan Thanh Nguyen;Van-Dinh Nguyen;Hieu V. Nguyen;Hien Quoc Ngo;A. Lee Swindlehurst;Markku Juntti

{"title":"Performance Analysis and Power Allocation for Massive MIMO ISAC Systems","authors":"Nhan Thanh Nguyen;Van-Dinh Nguyen;Hieu V. Nguyen;Hien Quoc Ngo;A. Lee Swindlehurst;Markku Juntti","doi":"10.1109/TSP.2025.3554012","DOIUrl":"10.1109/TSP.2025.3554012","url":null,"abstract":"Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming gains for both functionalities. In this work, we consider a massive MIMO-ISAC system employing a uniform planar array with zero-forcing and maximum-ratio downlink transmission schemes combined with monostatic radar-type sensing. Our focus lies on deriving closed form expressions for the achievable communications rate and the Cramér–Rao lower bound (CRLB), which serve as performance metrics for communications and sensing operations, respectively. The expressions enable us to investigate important operational characteristics of massive MIMO-ISAC, including the mutual effects of communications and sensing as well as the advantages stemming from using a very large antenna array for each functionality. Furthermore, we devise a power allocation strategy based on successive convex approximation to maximize the communications rate while guaranteeing the CRLB constraints and transmit power budget. Extensive numerical results are presented to validate our theoretical analyses and demonstrate the efficiency of the proposed power allocation approach.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1691-1707"},"PeriodicalIF":4.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713044","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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models GraphGrad：一般状态空间模型的稀疏多项式表示的有效估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-26 DOI: 10.1109/TSP.2025.3554876

Benjamin Cox;Émilie Chouzenoux;Víctor Elvira

{"title":"GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models","authors":"Benjamin Cox;Émilie Chouzenoux;Víctor Elvira","doi":"10.1109/TSP.2025.3554876","DOIUrl":"10.1109/TSP.2025.3554876","url":null,"abstract":"State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Instead, a sequence of observations related to the state is available. The SSM is defined by the state dynamics and the observation model, both of which are described by parametric distributions. Estimation of parameters of these distributions is a very challenging, but essential, task for performing inference and prediction. Furthermore, it is typical that not all states of the system interact. We can therefore encode the interaction of the states via a graph, usually not fully connected. However, most parameter estimation methods do not take advantage of this feature. In this work, we propose GraphGrad, a fully automatic approach for obtaining sparse estimates of the state interactions of a non-linear SSM via a polynomial approximation. This novel methodology unveils the latent structure of the data-generating process, allowing us to infer both the structure and value of a rich and efficient parameterisation of a general SSM. Our method utilises a differentiable particle filter to optimise a Monte Carlo likelihood estimator. It also promotes sparsity in the estimated system through the use of suitable proximity updates, known to be more efficient and stable than subgradient methods. As shown in our paper, a number of well-known dynamical systems can be accurately represented and recovered by our method, providing basis for application to real-world scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1562-1576"},"PeriodicalIF":4.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713043","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 Graph Learning From Smooth Data: A Bayesian Framework 平滑数据的分布式图学习：贝叶斯框架

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-25 DOI: 10.1109/TSP.2025.3553915

Jiayin Zhang;Nan Wu;Tingting Zhang;Bin Li;Qinsiwei Yan;Xiaoli Ma

{"title":"Distributed Graph Learning From Smooth Data: A Bayesian Framework","authors":"Jiayin Zhang;Nan Wu;Tingting Zhang;Bin Li;Qinsiwei Yan;Xiaoli Ma","doi":"10.1109/TSP.2025.3553915","DOIUrl":"10.1109/TSP.2025.3553915","url":null,"abstract":"The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in Graph Signal Processing (GSP) and finds applications in various data processing domains. However, the existing approaches have primarily focused on learning deterministic graphs, and thus are not suitable for applications involving topological stochasticity, such as epidemiological models. In this paper, we develop a hierarchical Bayesian model for graph learning problem. Specifically, the generative model of smooth signals is formulated by transforming the graph topology into self-expressiveness coefficients and incorporating individual noise for each vertex. Tailored probability distributions are imposed on each edge to characterize the valid graph topology constraints along with edge-level probabilistic information. Building upon this, we derive the Bayesian Graph Learning (BGL) approach to efficiently estimate the graph structure in a distributed manner. In particular, based on the specific probabilistic dependencies, we derive a series of message passing rules by a mixture of Generalized Approximate Message Passing (GAMP) message and Belief Propagation (BP) message to iteratively approximate the posterior probabilities. Numerical experiments with both artificial and real data demonstrate that BGL learns more accurate graph structures and enhances machine learning tasks compared to state-of-the-art methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1626-1642"},"PeriodicalIF":4.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713045","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

Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO 无小区大规模MIMO的可扩展多元前传量化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-24 DOI: 10.1109/TSP.2025.3550469

Sangwoo Park;Ahmet Hasim Gokceoglu;Li Wang;Osvaldo Simeone

{"title":"Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO","authors":"Sangwoo Park;Ahmet Hasim Gokceoglu;Li Wang;Osvaldo Simeone","doi":"10.1109/TSP.2025.3550469","DOIUrl":"10.1109/TSP.2025.3550469","url":null,"abstract":"The conventional approach to the fronthaul design for cell-free massive MIMO system follows the compress-and-precode (CP) paradigm. Accordingly, encoded bits and precoding coefficients are shared by the distributed unit (DU) on the fronthaul links, and precoding takes place at the radio units (RUs). Previous theoretical work has shown that CP can be potentially improved by a significant margin by <italic>precode-and-compress</i> (PC) methods, in which all baseband processing is carried out at the DU, which compresses the precoded signals for transmission on the fronthaul links. The theoretical performance gain of PC methods are particularly pronounced when the DU implements multivariate quantization (MQ), applying joint quantization across the signals for all the RUs. However, existing solutions for MQ are characterized by a computational complexity that grows exponentially with the sum-fronthaul capacity from the DU to all RUs. In this work, we first present <inline-formula><tex-math>$alpha$</tex-math></inline-formula>-parallel MQ (<inline-formula><tex-math>$alpha$</tex-math></inline-formula>-PMQ), a novel MQ scheme whose complexity for quantization is exponential in the fronthaul capacity towards individual RUs. <inline-formula><tex-math>$alpha$</tex-math></inline-formula>-PMQ tailors MQ to the topology of the network by allowing for parallel local quantization steps for RUs that do not interfere too much with each other. The performance of <inline-formula><tex-math>$alpha$</tex-math></inline-formula>-PMQ is seen to be close to exact MQ in the regime when both schemes are feasible. We then introduce neural MQ, which replaces the exhaustive search in MQ with gradient-based updates for a neural-network-based decoder, attaining a quantization complexity that grows linearly with the sum-fronthaul capacity. This makes neural-MQ the first truly scalable MQ strategy. Numerical results demonstrate that neural-MQ outperforms CP across all values of the fronthaul capacity regimes.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1658-1673"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702744","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

Convolutional Filtering With RKHS Algebras RKHS代数的卷积滤波

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-23 DOI: 10.1109/TSP.2025.3572861

Alejandro Parada-Mayorga;Leopoldo Agorio;Alejandro Ribeiro;Juan Bazerque

{"title":"Convolutional Filtering With RKHS Algebras","authors":"Alejandro Parada-Mayorga;Leopoldo Agorio;Alejandro Ribeiro;Juan Bazerque","doi":"10.1109/TSP.2025.3572861","DOIUrl":"10.1109/TSP.2025.3572861","url":null,"abstract":"In this paper, we develop a generalized theory of convolutional signal processing and neural networks for Reproducing Kernel Hilbert Spaces (RKHS). Leveraging the theory of algebraic signal processing (ASP), we show that any RKHS allows the formal definition of multiple algebraic convolutional models. We show that any RKHS induces algebras whose elements determine convolutional operators acting on RKHS elements. This approach allows us to achieve scalable filtering and learning as a byproduct of the convolutional model, and simultaneously take advantage of the well-known benefits of processing information in an RKHS. To emphasize the generality and usefulness of our approach, we show how algebraic RKHS can be used to define convolutional signal models on groups, graphons, and traditional Euclidean signal spaces. Furthermore, using algebraic RKHS models, we build convolutional networks, formally defining the notion of pointwise nonlinearities and deriving explicit expressions for the training. Such derivations are obtained in terms of the algebraic representation of the RKHS. We present a set of numerical experiments on real data in which wireless coverage is predicted from measurements captured by unmaned aerial vehicles. This particular real-life scenario emphasizes the benefits of the convolutional RKHS models in neural networks compared to fully connected and standard convolutional operators.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2353-2367"},"PeriodicalIF":4.6,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130579","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