IEEE Transactions on Signal Processing最新文献_第3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 5.4 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

引用次数: 0

Inverse Particle Filter 逆粒子滤波

IF 5.4 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

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

Generalization of Geometric Graph Neural Networks With Lipschitz Loss Functions 用 Lipschitz 损失函数泛化几何图神经网络

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-21 DOI: 10.1109/TSP.2025.3553378

Zhiyang Wang;Juan Cerviño;Alejandro Ribeiro

引用次数: 0

Globally Optimal Max-Min Rate Joint Channel and Power Allocation for Hybrid NOMA-OMA Downlink Systems 混合NOMA-OMA下行系统全局最优最大最小速率联合信道和功率分配

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-19 DOI: 10.1109/TSP.2025.3553084

Tanin Sultana;Sorina Dumitrescu

{"title":"Globally Optimal Max-Min Rate Joint Channel and Power Allocation for Hybrid NOMA-OMA Downlink Systems","authors":"Tanin Sultana;Sorina Dumitrescu","doi":"10.1109/TSP.2025.3553084","DOIUrl":"10.1109/TSP.2025.3553084","url":null,"abstract":"This work proposes a globally optimal solution algorithm to the joint power allocation (PA) and channel allocation (CA) problem for downlink hybrid NOMA-OMA systems with the objective of maximizing the minimum user rate. In the hybrid NOMA-OMA scenario, the users are divided into clusters, each cluster shares one channel using NOMA (Non-Orthogonal Multiple Access), while different clusters are assigned channels orthogonally. The optimization problem is converted to the problem of maximizing the user rate under the constraint that all rates be equal. It is further decomposed into PA and CA subproblems, which are solved iteratively. The PA subproblem is handled by first deriving an analytical expression of the total power as a function of the common user rate, and then solving it via bisection search. The CA subproblem keeps the equal-rate assignment fixed and aims to find the CA that minimizes the total power. We prove that the CA subproblem is equivalent to a minimum bipartite graph matching problem, for which efficient algorithms exist. Finally, we demonstrate that the proposed iterative algorithm converges to the globally optimal solution after a finite number of iterations. In addition, we prove that the number of iterations is at most three when the power budget is sufficiently large. Extensive experiments demonstrate the effectiveness of the proposed scheme.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1674-1690"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661538","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

Direct Multipath-Based SLAM 直接多路径SLAM

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-19 DOI: 10.1109/tsp.2025.3552747

Mingchao Liang, Erik Leitinger, Florian Meyer

引用次数: 0