IEEE Transactions on Signal Processing最新文献_第8页

Fast Real-Time Arbitrary Waveform Generation Using Graphic Processing Units 快速实时任意波形生成使用图形处理单元

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-28 DOI: 10.1109/TSP.2025.3574958

Juntian Tu;Sarthak Subhankar

{"title":"Fast Real-Time Arbitrary Waveform Generation Using Graphic Processing Units","authors":"Juntian Tu;Sarthak Subhankar","doi":"10.1109/TSP.2025.3574958","DOIUrl":"10.1109/TSP.2025.3574958","url":null,"abstract":"Real-time arbitrary waveform generation (AWG) is essential in various engineering and research applications. This paper introduces a novel AWG architecture using an NVIDIA graphics processing unit (GPU) and a commercially available high-speed digital-to-analog converter (DAC) card, both running on a desktop personal computer (PC). The GPU accelerates the “embarrassingly” data-parallel additive synthesis framework for AWG, and the DAC reconstructs the generated waveform in the analog domain at high speed. The AWG software is developed using the developer-friendly compute unified device architecture (CUDA) runtime application programming interface (API) from NVIDIA. With this architecture, we achieve a 586-fold increase in the speed of computing periodic radio-frequency (rf) arbitrary waveforms compared to a central processing unit (CPU). We also demonstrate two different pathways for dynamically controlling multi-tone rf waveforms, which we characterize by chirping individual single-frequency tones in the multi-tone waveforms. One pathway offers arbitrary simultaneous chirping of 1000 individual Nyquist-limited single-frequency tones at a sampling rate of 280 megasamples per second (MS/s) for a limited time duration of 35 ms. The other pathway offers simultaneous chirping of 340 individual Nyquist-limited single-frequency tones at 50 MS/s, or 55 individual tones at 280 MS/s for an arbitrary duration. Using the latter pathway, we demonstrate control over 5000-tone and 10,000-tone waveforms by chirping all of their constituent tones in groups of up to 100 tones. This AWG architecture is designed for creating large defect-free optical tweezer arrays of single neutral atoms or molecules for quantum simulation and quantum computation.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2368-2382"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164975","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

Extended Object Tracking by Rao-Blackwellized Particle Filtering for Orientation Estimation 基于rao - blackwelzed粒子滤波的扩展目标跟踪方向估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-28 DOI: 10.1109/TSP.2025.3574689

Simon Steuernagel;Marcus Baum

引用次数: 0

Outlier-Robust Centralized and Distributed Variational Bayesian Moving Horizon Estimation 离群鲁棒集中分布变分贝叶斯移动水平估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-27 DOI: 10.1109/TSP.2025.3572912

Zihao Jiang;Giorgio Battistelli;Luigi Chisci;Nicola Forti;Weidong Zhou

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

Linear Convergence of Plug-and-Play Algorithms With Kernel Denoisers 核去噪即插即用算法的线性收敛性

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-26 DOI: 10.1109/TSP.2025.3573044

Arghya Sinha;Bhartendu Kumar;Chirayu D. Athalye;Kunal N. Chaudhury

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