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

Byzantine-Robust and Communication-Efficient Personalized Federated Learning 拜占庭鲁棒和通信高效的个性化联邦学习

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-11 DOI: 10.1109/TSP.2024.3514802

Jiaojiao Zhang;Xuechao He;Yue Huang;Qing Ling

引用次数: 0

Corrections to “DoA Estimation for Hybrid Receivers: Full Spatial Coverage and Successive Refinement” 对“混合接收机的DoA估计：全空间覆盖和逐次细化”的修正

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-11 DOI: 10.1109/TSP.2024.3499772

Ali Abdelbadie;Mona Mostafa;Salime Bameri;Ramy H. Gohary;Dimple Thomas

引用次数: 0

Robust Phase Retrieval by Alternating Minimization 交替最小化鲁棒相位恢复

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-11 DOI: 10.1109/TSP.2024.3515008

Seonho Kim;Kiryung Lee

引用次数: 0

Spatially Non-Stationary XL-MIMO Channel Estimation: A Three-Layer Generalized Approximate Message Passing Method 空间非平稳xml - mimo信道估计：一种三层广义近似消息传递方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-09 DOI: 10.1109/TSP.2024.3512575

Anzheng Tang;Jun-Bo Wang;Yijin Pan;Wence Zhang;Yijian Chen;Hongkang Yu;Rodrigo C. de Lamare

{"title":"Spatially Non-Stationary XL-MIMO Channel Estimation: A Three-Layer Generalized Approximate Message Passing Method","authors":"Anzheng Tang;Jun-Bo Wang;Yijin Pan;Wence Zhang;Yijian Chen;Hongkang Yu;Rodrigo C. de Lamare","doi":"10.1109/TSP.2024.3512575","DOIUrl":"10.1109/TSP.2024.3512575","url":null,"abstract":"In this paper, the channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of near-field (NF) spherical wavefront effects and spatially non-stationary (SnS) properties. Due to the diversity of SnS characteristics across different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase estimation scheme that decouples the problem into multiple subchannel estimation tasks. To solve these sub-tasks, we introduce a novel three-layer Bayesian inference scheme, exploiting the correlations and sparsity of the SnS subchannels in both the spatial and angular domains. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS properties in the spatial domain, and the third layer effectively decouples each subchannel from the observed signal. To enable efficient Bayesian inference, we develop a three-layer generalized approximate message passing (TL-GAMP) algorithm that combines structured variational message passing with belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed TL-GAMP algorithm, demonstrating its robustness across various channel environments, including NF-SnS, NF spatially stationary (NF-SS), and far-field spatially stationary (FF-SS) scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"356-371"},"PeriodicalIF":4.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796929","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

Graph Linear Canonical Transform: Definition, Vertex-Frequency Analysis and Filter Design 图线性正则变换：定义、顶点频率分析和滤波器设计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-05 DOI: 10.1109/TSP.2024.3507787

Jian Yi Chen;Yu Zhang;Bing Zhao Li

引用次数: 0

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms 多模态数据融合的个性化耦合张量分解：唯一性与算法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-03 DOI: 10.1109/TSP.2024.3510680

Ricardo A. Borsoi;Konstantin Usevich;David Brie;Tülay Adali

{"title":"Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms","authors":"Ricardo A. Borsoi;Konstantin Usevich;David Brie;Tülay Adali","doi":"10.1109/TSP.2024.3510680","DOIUrl":"10.1109/TSP.2024.3510680","url":null,"abstract":"Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important challenges of data fusion, where: 1) the datasets are often heterogeneous, constituting different “views” of a given phenomena (multimodality); and 2) each dataset can contain \u0000<italic>personalized</i>\u0000 or dataset-specific information, constituting distinct factors that are not coupled with other datasets. In this work, we introduce a personalized CTD framework tackling these challenges. A flexible model is proposed where each dataset is represented as the sum of two components, one related to a common tensor through a multilinear measurement model, and another specific to each dataset. Both the common and distinct components are assumed to admit a polyadic decomposition. This generalizes several existing CTD models. We provide conditions for specific and generic uniqueness of the decomposition that are easy to interpret. These conditions employ \u0000<italic>uni-mode</i>\u0000 uniqueness of different individual datasets and properties of the measurement model. Two algorithms are proposed to compute the common and distinct components: a semi-algebraic one and a coordinate-descent optimization method. Experimental results illustrate the advantage of the proposed framework compared with the state of the art approaches.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"113-129"},"PeriodicalIF":4.6,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776540","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

Reliable Robust Adaptive Steganographic Coding Based on Nested Polar Codes 基于嵌套极码的可靠鲁棒自适应隐写编码

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-03 DOI: 10.1109/TSP.2024.3510755

Qiyi Yao;Kai Zeng;Weiming Zhang;Kejiang Chen

{"title":"Reliable Robust Adaptive Steganographic Coding Based on Nested Polar Codes","authors":"Qiyi Yao;Kai Zeng;Weiming Zhang;Kejiang Chen","doi":"10.1109/TSP.2024.3510755","DOIUrl":"10.1109/TSP.2024.3510755","url":null,"abstract":"Steganography is the art of covert communication that pursues the secrecy of concealment. In adaptive steganography, the most commonly used framework of steganography, the sender embeds a “secret message” signal within another “cover” signal with respect to a certain adaptive distortion function that measures the distortion incurred, contributing to the composite “stego” signal that resembles the cover, and the receiver extracts the “secret message” signal from the stego. When the communication channel between the sender and the receiver is noisy, robust steganography is needed, in which robust adaptive steganographic coding plays a central role. The existing robust adaptive steganographic coding methods can only provide very limited robustness, and they fail when the communication channel is bad. To ensure the success of covert communication, we propose a reliable robust adaptive steganographic coding scheme based on nested polar codes that possesses the highest robustness among the existing algorithms while the security performance is also maintained. Theoretically, we show that for the most important \u0000<italic>binary embedding</i>\u0000, in the special case where the communication channel is a Binary Symmetric Channel (BSC), the proposed scheme is optimal under the constant distortion profile as the cover length \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 tends to infinity through powers of two when the \u0000<italic>design embedding rate</i>\u0000 is large enough. Experimentally, our method is capable of making sure the perfect extraction of the secret message in situations where the embedding rate is large or the communication channel is bad, while the existing algorithms are not applicable in these scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"12-25"},"PeriodicalIF":4.6,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776569","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

WSS Processes and Wiener Filters on Digraphs 有向图上的WSS过程和Wiener滤波器

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-03 DOI: 10.1109/TSP.2024.3510434

Mohammad Bagher Iraji;Mohammad Eini;Arash Amini

引用次数: 0

Generalized Bilinear Factorization via Hybrid Vector Message Passing 基于混合向量消息传递的广义双线性分解

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-02 DOI: 10.1109/TSP.2024.3509413

Hao Jiang;Xiaojun Yuan;Qinghua Guo

{"title":"Generalized Bilinear Factorization via Hybrid Vector Message Passing","authors":"Hao Jiang;Xiaojun Yuan;Qinghua Guo","doi":"10.1109/TSP.2024.3509413","DOIUrl":"10.1109/TSP.2024.3509413","url":null,"abstract":"Generalized bilinear factorization (GBF), in which two matrices are recovered from noisy and typically compressed measurements of their product, arises in various applications such as blind channel-and-signal estimation, image completion, and compressed video foreground and background separation. In this paper, we formulate the GBF problem by unifying several existing bilinear inverse problems, and establish a novel hybrid vector message passing (HVMP) algorithm for GBF. The GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, and exchanges matrix-variable messages in closed form. GBF-HVMP is advantageous over its counterparts in several aspects. For example, a matrix-variable message can characterize the correlations between the elements of the matrix, which is not possible in scalar-variable message passing; the hybrid of EP and VMP yields closed-form Gaussian messages associated with the bilinear constraints inherent in the GBF problem. We show that damping is unnecessary for GBF-HVMP to ensure convergence. We also show that GBF-HVMP performs close to the replica bound, and significantly outperforms state-of-the-art approaches in terms of both normalized mean squared error (NMSE) performance and computational complexity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5675-5690"},"PeriodicalIF":4.6,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142760268","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

Narrowband Interference Cancellation for OFDM Based on Deep Learning and Compressed Sensing 基于深度学习和压缩感知的OFDM窄带干扰消除

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-02 DOI: 10.1109/tsp.2024.3510623

Yue Hu, Songkang Huang, Lei Zhao, Ming Jiang

引用次数: 0