IEEE Transactions on Signal Processing最新文献_第10页

Proportionally Fair Joint Power and Channel Allocation for Hybrid NOMA-OMA Downlink Systems 混合NOMA-OMA下行系统的比例公平联合功率和信道分配

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-03 DOI: 10.1109/TSP.2025.3584665

Tanin Sultana;Sorina Dumitrescu

{"title":"Proportionally Fair Joint Power and Channel Allocation for Hybrid NOMA-OMA Downlink Systems","authors":"Tanin Sultana;Sorina Dumitrescu","doi":"10.1109/TSP.2025.3584665","DOIUrl":"10.1109/TSP.2025.3584665","url":null,"abstract":"We consider a downlink multiuser transmission system that divides users into clusters, each cluster shares one channel using non-orthogonal multiple access (NOMA), while different clusters are assigned orthogonal channels. To achieve high system efficiency while guaranteeing fairness, we propose a joint power allocation (PA) and channel allocation (CA) framework with the proportional fairness (PF) objective, which maximizes the sum of logarithmic rates. The problem is decoupled into the PA and CA subproblems, which are solved iteratively. For the PA subproblem, we prove that although it is not convex, strong duality holds and the problem can be solved globally optimally by solving the KKT conditions. We further propose a <inline-formula><tex-math>$O(T(logfrac{1}{epsilon})^{2})$</tex-math></inline-formula> time algorithm for this purpose, where <inline-formula><tex-math>$T$</tex-math></inline-formula> is the number of users and <inline-formula><tex-math>$epsilon$</tex-math></inline-formula> is the tolerance threshold. The PA problem with the PF objective was considered before only for one NOMA group. When specialized to this case, our algorithm is much faster than in prior work. For the CA subproblem, we prove that it is equivalent to a bipartite graph matching problem, for which efficient solution algorithms exist. We show empirically that the proposed joint PA-CA approach performs very close to exhaustive search for small number of users. Extensive experiments demonstrate that our framework significantly outperforms several benchmark schemes in both system efficiency and fairness.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3156-3172"},"PeriodicalIF":5.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566116","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

Decentralized Variational Inference Frameworks for Multi-Object Tracking on Sensor Networks 传感器网络中多目标跟踪的分散变分推理框架

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-02 DOI: 10.1109/TSP.2025.3584248

Qing Li;Runze Gan;Simon J. Godsill

{"title":"Decentralized Variational Inference Frameworks for Multi-Object Tracking on Sensor Networks","authors":"Qing Li;Runze Gan;Simon J. Godsill","doi":"10.1109/TSP.2025.3584248","DOIUrl":"10.1109/TSP.2025.3584248","url":null,"abstract":"This paper tackles the challenge of multi-sensor multi-object tracking by proposing various decentralised Variational Inference (VI) schemes that match the tracking performance of centralised sensor fusion with only local message exchanges among neighboring sensors. We first establish a centralised VI sensor fusion scheme as a benchmark and analyse the limitations of its decentralised counterpart, which requires sensors to await consensus at each VI iteration. Therefore, we propose a decentralised gradient-based VI framework that optimises the Locally Maximised Evidence Lower Bound (LM-ELBO) instead of the standard ELBO, which reduces the parameter search space and enables faster convergence, making it particularly beneficial for decentralised tracking. This proposed framework is inherently self-evolving, improving with advances in decentralised optimisation techniques for convergence guarantees and efficiency. Further, we enhance the convergence speed of proposed decentralised schemes using natural gradients and gradient tracking strategies. Results verify that our decentralised VI schemes are empirically equivalent to centralised fusion in tracking performance. Notably, the decentralised natural gradient VI method is the most communication-efficient, with communication costs comparable to suboptimal decentralised strategies while delivering notably higher tracking accuracy.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2753-2767"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547006","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

Hybrid Population Monte Carlo 混合种群蒙特卡罗

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-30 DOI: 10.1109/TSP.2025.3583988

Ali Mousavi;Víctor Elvira

{"title":"Hybrid Population Monte Carlo","authors":"Ali Mousavi;Víctor Elvira","doi":"10.1109/TSP.2025.3583988","DOIUrl":"10.1109/TSP.2025.3583988","url":null,"abstract":"Importance sampling (IS) is a powerful Monte Carlo (MC) technique for approximating intractable integrals, for instance in Bayesian inference. The performance of IS relies heavily on the appropriate choice of the so-called proposal distribution. Adaptive IS (AIS) methods iteratively improve target estimates by adapting the proposal distribution. Recent AIS research focuses on enhancing proposal adaptation for high-dimensional problems, while addressing the challenge of multi-modal targets. In this paper, a new class of AIS methods is presented, utilizing a hybrid approach that incorporates weighted samples and proposal distributions to enhance performance. This approach belongs to the family of population Monte Carlo (PMC) algorithms, where a population of proposals is adapted to better approximate the target distribution. The proposed hybrid population Monte Carlo (HPMC) implements a novel two-step adaptation mechanism. In the first step, a hybrid method is used to generate the population of the preliminary proposal locations based on both weighted samples and location parameters. We use Hamiltonian Monte Carlo (HMC) to generate the preliminary proposal locations. HMC has a good exploratory behavior, especially in high dimension scenarios. In the second step, the novel cooperation algorithms are performing to find the final proposals for the next iteration. HPMC achieves a significant performance improvement in high-dimensional problems when compared to the state-of-the-art algorithms. We discuss the statistical properties of HPMC and show its high performance in two challenging benchmarks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2676-2687"},"PeriodicalIF":4.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520578","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

Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured Signals kronecker结构信号的有效离网贝叶斯参数估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-30 DOI: 10.1109/TSP.2025.3583895

Yanbin He;Geethu Joseph

引用次数: 0

A Radio Map Approach for Reduced Pilot CSI Tracking in Massive MIMO Networks 大规模MIMO网络中减少导频CSI跟踪的无线电地图方法

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-30 DOI: 10.1109/tsp.2025.3584229

Yuanshuai Zheng, Junting Chen

引用次数: 0

FieldFormer: Self-Supervised Reconstruction of Physical Fields via Tensor Attention Prior FieldFormer：基于张量注意先验的自监督物理场重建

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-27 DOI: 10.1109/TSP.2025.3580374

Panqi Chen;Siyuan Li;Lei Cheng;Xiao Fu;Yik-Chung Wu;Sergios Theodoridis

{"title":"FieldFormer: Self-Supervised Reconstruction of Physical Fields via Tensor Attention Prior","authors":"Panqi Chen;Siyuan Li;Lei Cheng;Xiao Fu;Yik-Chung Wu;Sergios Theodoridis","doi":"10.1109/TSP.2025.3580374","DOIUrl":"10.1109/TSP.2025.3580374","url":null,"abstract":"Reconstructing physical field tensors from <italic>in situ</i> observations, such as radio maps and ocean sound speed fields, is crucial for enabling environment-aware decision making in various applications, e.g., wireless communications and underwater acoustics. Field data reconstruction is often challenging, due to the limited and noisy nature of the observations, necessitating the incorporation of prior information to aid the reconstruction process. Deep neural network-based data-driven structural constraints (e.g., “deeply learned priors”) have showed promising performance. However, this family of techniques faces challenges such as model mismatches between training and testing phases. This work introduces FieldFormer, a self-supervised neural prior learned solely from the limited <italic>in situ</i> observations without the need of offline training. Specifically, the proposed framework starts with modeling the fields of interest using the tensor Tucker model of a high multilinear rank, which ensures a universal approximation property for all fields. In the sequel, an attention mechanism is incorporated to learn the sparsity pattern that underlies the core tensor in order to reduce the solution space. In this way, a “complexity-adaptive” neural representation, grounded in the Tucker decomposition, is obtained that can flexibly represent various types of fields. A theoretical analysis is provided to support the recoverability of the proposed design. Moreover, extensive experiments, using various physical field tensors, demonstrate the superiority of the proposed approach compared to state-of-the-art baselines. The code is available at <uri>https://github.com/OceanSTARLab/FieldFormer</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2704-2718"},"PeriodicalIF":4.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503341","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

Infinite Factorial Linear Dynamical Systems for Transient Signal Detection 用于暂态信号检测的无穷阶乘线性动力系统

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-27 DOI: 10.1109/TSP.2025.3582215

Jiadi Bao;Yatong Wang;Yunjie Li;Mengtao Zhu;Shafei Wang

{"title":"Infinite Factorial Linear Dynamical Systems for Transient Signal Detection","authors":"Jiadi Bao;Yatong Wang;Yunjie Li;Mengtao Zhu;Shafei Wang","doi":"10.1109/TSP.2025.3582215","DOIUrl":"10.1109/TSP.2025.3582215","url":null,"abstract":"Accurately detecting the transient signal of interest from the background signal is one of the fundamental tasks in signal processing. The most recent approaches assume the existence of a single background source and represent the background signal using a linear dynamical system, but this assumption might fail to capture the complexities of modern electromagnetic environments with multiple sources. To address this limitation, this paper proposes a method for detecting the transient signal in a background composed of an unknown number of emitters. The proposed method consists of two main tasks. First, a Bayesian nonparametric model called the infinite factorial linear dynamical systems is developed. The developed model is based on the Markov Indian buffet process and enables the representation and parameter learning of an unbounded number of background sources. This study also designs a parameter learning method for the infinite factorial linear dynamical systems using slice sampling and particle Gibbs with ancestor sampling. Second, a theoretically straightforward generalized likelihood ratio stopping time is defined, but it is computationally infeasible for factorial linear dynamical systems. To facilitate the computation, we derive the factorial Kalman forward filtering method and design a dependence structure for the underlying model, enabling the stopping time to be defined recursively. Then, the statistical performance of the proposed stopping time is investigated. Numerical simulations demonstrate the effectiveness of the proposed method and the validity of the theoretical results. The experimental results of the pulse signal detection under the condition of communication interference confirm the effectiveness and superiority of the proposed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2574-2589"},"PeriodicalIF":4.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503342","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

New Restricted Isometry Property Analysis for $ell_{1}$-$alphaell_{2}$ Minimization 1-α 2最小化的新限制等距性质分析

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-25 DOI: 10.1109/TSP.2025.3582921

Haifeng Li;Leiyan Guo;Jinming Wen

{"title":"New Restricted Isometry Property Analysis for $ell_{1}$-$alphaell_{2}$ Minimization","authors":"Haifeng Li;Leiyan Guo;Jinming Wen","doi":"10.1109/TSP.2025.3582921","DOIUrl":"10.1109/TSP.2025.3582921","url":null,"abstract":"In this paper, we consider the <inline-formula><tex-math>$ell_{1}$</tex-math></inline-formula>-<inline-formula><tex-math>$alphaell_{2}$</tex-math></inline-formula> minimization method for <inline-formula><tex-math>$alphain(0,2]$</tex-math></inline-formula>. Specifically, we present sparse signal recovery conditions under two types of noise: <inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula>-bounded noise (<inline-formula><tex-math>$|mathbf{v}|_{2},{boldsymbolleq},epsilon$</tex-math></inline-formula> for some constant <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>) and <inline-formula><tex-math>$ell_{boldsymbolinfty}$</tex-math></inline-formula>-bounded noise (<inline-formula><tex-math>$|mathbf{A}^{T}mathbf{v}|_{boldsymbolinfty},{boldsymbolleq},epsilon$</tex-math></inline-formula> for some constant <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>). First, based on the RIP framework, we provide a new theoretical guarantee for the successful recovery of sparse signals in the presence of noise when <inline-formula><tex-math>$alpha,{boldsymbolin},(0,1]$</tex-math></inline-formula>, and show that our results are superior to existing ones. Second, we extend the range of <inline-formula><tex-math>$alpha$</tex-math></inline-formula> to <inline-formula><tex-math>$(1,2]$</tex-math></inline-formula> and provide a new theoretical guarantee for sparse signal recovery under the RIP framework. Finally, numerical experiments demonstrate that the recovery success rate for <inline-formula><tex-math>$alpha,{boldsymbolin},(1,2]$</tex-math></inline-formula> is higher than that for <inline-formula><tex-math>$alpha,{boldsymbolin},(0,1]$</tex-math></inline-formula>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2787-2802"},"PeriodicalIF":5.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144488519","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

Optimal Beamforming Structure and Efficient Optimization Algorithms for Generalized Multi-Group Multicast Beamforming Optimization 广义多组多播波束形成优化的最优波束形成结构和高效优化算法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-25 DOI: 10.1109/TSP.2025.3581486

Tianyu Fang;Yijie Mao

{"title":"Optimal Beamforming Structure and Efficient Optimization Algorithms for Generalized Multi-Group Multicast Beamforming Optimization","authors":"Tianyu Fang;Yijie Mao","doi":"10.1109/TSP.2025.3581486","DOIUrl":"10.1109/TSP.2025.3581486","url":null,"abstract":"In this work, we focus on solving non-smooth non-convex maximization problems in multi-group multicast transmission. By leveraging Karush-Kuhn-Tucker (KKT) optimality conditions, we thoroughly analyze the optimal beamforming structure for a set of optimization problems characterized by a general utility-based objective function. By exploiting the identified optimal structure, we further unveil inherent low-dimensional beamforming structures within the problems, which are asymptotically optimal in various regimes of transmit signal-to-noise ratios (SNRs) or the number of transmit antennas. Building upon the discovered optimal and low-dimensional beamforming structures, we then propose highly efficient optimization algorithms to solve a specific multi-group multicast optimization problem based on the weighted power mean (WPM) utility function. The proposed algorithms first use the successive convex approximation (SCA) framework to decompose the problem into a sequence of convex subproblems, each with an optimal closed-form beamforming solution structure. Then, we propose a hyperplane fixed point iteration (HFPI) algorithm to compute the optimal Lagrangian dual variables for each subproblem. Numerical results show that the proposed algorithms maintain comparable or improved utility performance compared to baseline algorithms, while dramatically reducing the computational complexity. Notably, the proposed ultra-low-complexity algorithms based on low-dimensional beamforming structures achieve near optimal utility performance with extremely low computational complexity. This complexity remains independent of the number of transmit antennas, making them promising and practical for extremely large multiple-input multiple-output (XL-MIMO) applications in 6G.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2719-2735"},"PeriodicalIF":4.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144488518","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

SINR Maximizing Distributionally Robust Adaptive Beamforming 最大SINR分布鲁棒自适应波束形成

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-24 DOI: 10.1109/TSP.2025.3582396

Kiarash Hassas Irani;Yongwei Huang;Sergiy A. Vorobyov

{"title":"SINR Maximizing Distributionally Robust Adaptive Beamforming","authors":"Kiarash Hassas Irani;Yongwei Huang;Sergiy A. Vorobyov","doi":"10.1109/TSP.2025.3582396","DOIUrl":"10.1109/TSP.2025.3582396","url":null,"abstract":"This paper addresses the robust adaptive beamforming (RAB) problem via the worst-case signal-to-interference-plus-noise ratio (SINR) maximization over distributional uncertainty sets for the random interference-plus-noise covariance (INC) matrix and desired signal steering vector. Our study explores two distinct uncertainty sets for the INC matrix and three for the steering vector. The uncertainty sets of the INC matrix account for the support and the positive semidefinite (PSD) mean of the distribution, as well as a similarity constraint on the mean. The uncertainty sets for the steering vector consist of the constraints on the first- and second-order moments of its associated probability distribution. The RAB problem is formulated as the minimization of the worst-case expected value of the SINR denominator over any distribution within the uncertainty set of the INC matrix, subject to the condition that the expected value of the numerator is greater than or equal to one for every distribution within the uncertainty set of the steering vector. By leveraging the strong duality of linear conic programming, this RAB problem is reformulated as a quadratic matrix inequality problem. Subsequently, it is addressed by iteratively solving a sequence of linear matrix inequality relaxation problems, incorporating a penalty term for the rank-one PSD matrix constraint. We further analyze the convergence of the iterative algorithm. The proposed robust beamforming approach is validated through simulation examples, which illustrate improved performance in terms of the array output SINR.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2542-2557"},"PeriodicalIF":4.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11048751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479200","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