IEEE Journal of Selected Topics in Signal Processing最新文献_第2页

Guest Editorial Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems 超大规模天线阵列系统的分布式信号处理

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-03-02 DOI: 10.1109/JSTSP.2025.3542164

Tsung-Hui Chang;Eduard A. Jorswieck;Erik G. Larsson;Xiao Li;A. Lee Swindlehurst

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IEEE Signal Processing Society Information IEEE信号处理学会信息

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IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-03-02 DOI: 10.1109/JSTSP.2025.3562646

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Deep Minimax Classifiers for Imbalanced Datasets With a Small Number of Minority Samples 具有少量少数样本的不平衡数据集的深度极小极大分类器

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-28 DOI: 10.1109/JSTSP.2025.3546083

Hansung Choi;Daewon Seo

{"title":"Deep Minimax Classifiers for Imbalanced Datasets With a Small Number of Minority Samples","authors":"Hansung Choi;Daewon Seo","doi":"10.1109/JSTSP.2025.3546083","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3546083","url":null,"abstract":"The concept of a minimax classifier is well-established in statistical decision theory, but its implementation via neural networks remains challenging, particularly in scenarios with imbalanced training data having a limited number of samples for minority classes. To address this issue, we propose a novel minimax learning algorithm designed to minimize the risk of worst-performing classes. Our algorithm iterates through two steps: a minimization step that trains the model based on a selected target prior, and a maximization step that updates the target prior towards the adversarial prior for the trained model. In the minimization, we introduce a targeted logit-adjustment loss function that efficiently identifies optimal decision boundaries under the target prior. Moreover, based on a new prior-dependent generalization bound that we obtained, we theoretically prove that our loss function has a better generalization capability than existing loss functions. During the maximization, we refine the target prior by shifting it towards the adversarial prior, depending on the worst-performing classes rather than on per-class risk estimates. Our maximization method is particularly robust in the regime of a small number of samples. Additionally, to adapt to overparameterized neural networks, we partition the entire training dataset into two subsets: one for model training during the minimization step and the other for updating the target prior during the maximization step. Our proposed algorithm has a provable convergence property, and empirical results indicate that our algorithm performs better than or is comparable to existing methods.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"491-506"},"PeriodicalIF":8.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-27 DOI: 10.1109/JSTSP.2025.3539494

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IEEE Signal Processing Society Publication Information IEEE信号处理学会出版物信息

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-27 DOI: 10.1109/JSTSP.2025.3539490

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Online Neyman-Pearson Classification With Hierarchically Represented Models 基于层次表示模型的在线Neyman-Pearson分类

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-20 DOI: 10.1109/JSTSP.2025.3544024

Basarbatu Can;Soner Ozgun Pelvan;Huseyin Ozkan

{"title":"Online Neyman-Pearson Classification With Hierarchically Represented Models","authors":"Basarbatu Can;Soner Ozgun Pelvan;Huseyin Ozkan","doi":"10.1109/JSTSP.2025.3544024","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3544024","url":null,"abstract":"We consider the statistical anomaly detection problem with regard to false alarm rate (or false positive rate, FPR) controllability, nonlinear modeling and computational efficiency for real-time processing. A decision theoretical solution can be formulated as Neyman-Pearson (NP) hypothesis testing (binary classification: anomaly/nominal). In this framework, we propose an ensemble NP classifier (Tree OLNP) that is based on a binary partitioning tree. Tree OLNP generates an ensemble of sample space partitions. Each partition corresponds to an online piecewise linear (hence nonlinear) expert classifier as a union of online linear NP classifiers (union of OLNPs). While maintaining a precise control over the FPR, Tree OLNP generates its overall prediction as a performance driven and time varying weighted combination of the experts. This provides a dynamical nonlinear modeling power in the sense that simpler (more powerful) experts receive larger weights early (late) in the data stream, which manages the bias-variance trade-off and mitigates overfitting/underfitting issues. We mathematically prove that, for any stream, Tree OLNP asymptotically performs at least as well as of the best expert in terms of the NP performance with a regret diminishing in the order <inline-formula><tex-math>$O(1/sqrt{t})$</tex-math></inline-formula> (<inline-formula><tex-math>$t:$</tex-math></inline-formula> data size). Our algorithm is computationally highly efficient since it is online and its complexity scales linearly with respect to both the data size and tree depth, and scales twice-logarithmic with respect to the number of experts. We experimentally show that Tree OLNP strongly outperforms the state-of-the-art alternative techniques.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"478-490"},"PeriodicalIF":8.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Channel Map-Based Angle Domain Multiple Access for Cell-Free Massive MIMO Communications 无小区大规模MIMO通信中基于信道映射的角度域多址

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-13 DOI: 10.1109/JSTSP.2025.3536289

Shuaifei Chen;Cheng-Xiang Wang;Junling Li;Chen Huang;Hengtai Chang;Yusong Huang;Jie Huang;Yunfei Chen

{"title":"Channel Map-Based Angle Domain Multiple Access for Cell-Free Massive MIMO Communications","authors":"Shuaifei Chen;Cheng-Xiang Wang;Junling Li;Chen Huang;Hengtai Chang;Yusong Huang;Jie Huang;Yunfei Chen","doi":"10.1109/JSTSP.2025.3536289","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3536289","url":null,"abstract":"Being aware of the channel and its properties is critical for coherent transmission in massive multiple-input multiple-output (M-MIMO) systems due to the large channel dimension in the space domain. In cell-free (CF) systems, the channel dimension increases further as each user is served by multiple access points, with a significant burden on signal processing. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing a priori channel information through channel measurements and modeling, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for the uplink CF M-MIMO communications. First, we propose an angle domain data reception scheme constituting receive combining and large-scale fading decoding to maximize spectral efficiency. Then, we derive an initial access criterion utilizing the angle domain channel similarity between users, based on which we propose pilot assignment and access point selection schemes for better trade-offs between spectral and energy efficiency. Finally, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived spectral efficiency upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed channel map-based angle domain schemes outperform their space domain alternatives and the schemes without using channel maps regarding spectral and energy efficiency.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 2","pages":"366-380"},"PeriodicalIF":8.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity 6D移动天线的分布式信道估计与优化：揭示方向稀疏性

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/JSTSP.2025.3539085

Xiaodan Shao;Rui Zhang;Qijun Jiang;Jihong Park;Tony Q. S. Quek;Robert Schober

{"title":"Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity","authors":"Xiaodan Shao;Rui Zhang;Qijun Jiang;Jihong Park;Tony Q. S. Quek;Robert Schober","doi":"10.1109/JSTSP.2025.3539085","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3539085","url":null,"abstract":"Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and rotations of antennas/antenna surfaces based on the channel spatial distribution. To achieve optimal antenna positions and rotations, acquiring statistical channel state information (CSI) is essential for 6DMA systems. However, existing works assume that a central processing unit (CPU) jointly processes the signals of all 6DMA surfaces. This incurs prohibitively high processing cost and latency for channel estimation due to the vast numbers of 6DMA candidate positions/rotations and antenna elements. Therefore, we propose a distributed 6DMA processing architecture to reduce the processing complexity of the CPU by equipping each 6DMA surface with a local processing unit (LPU). Furthermore, we unveil for the first time the <bold><i>directional sparsity</i></b> property of the 6DMA channels with respect to distributed users, where each user has significant channel gains only for a (small) subset of 6DMA position-rotation pairs. Based on this property, we propose a practical three-stage protocol for the 6DMA system and corresponding algorithms to conduct statistical CSI acquisition for all 6DMA candidate positions/rotations, 6DMA position/rotation optimization based on statistical CSI, and instantaneous CSI estimation for user data transmission with optimized 6DMA positions/rotations. Simulation results show that the proposed channel estimation algorithms achieve higher accuracy than benchmark schemes, while requiring lower pilot overhead. Moreover, the proposed 6DMA system with statistical CSI-based position/rotation optimization achieves a higher ergodic sum rate than fixed-position and fluid antenna systems, even if the latter have perfect instantaneous CSI.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 2","pages":"349-365"},"PeriodicalIF":8.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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2024 Index IEEE Journal of Selected Topics in Signal Processing Vol. 18 2024索引IEEE信号处理精选主题杂志Vol. 18

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/JSTSP.2025.3541370

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Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions 超大规模天线阵列系统的分布式信号处理：最新技术和未来方向

IF 8.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/JSTSP.2025.3541386

Yanqing Xu;Erik G. Larsson;Eduard A. Jorswieck;Xiao Li;Shi Jin;Tsung-Hui Chang

{"title":"Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions","authors":"Yanqing Xu;Erik G. Larsson;Eduard A. Jorswieck;Xiao Li;Shi Jin;Tsung-Hui Chang","doi":"10.1109/JSTSP.2025.3541386","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3541386","url":null,"abstract":"Extremely large-scale antenna arrays (ELAA) play a critical role in enabling the functionalities of next generation wireless communication systems. However, as the number of antennas increases, ELAA systems face significant bottlenecks, such as excessive interconnection costs and high computational complexity. Efficient distributed signal processing (SP) algorithms show great promise in overcoming these challenges. In this paper, we provide a comprehensive overview of distributed SP algorithms for ELAA systems, tailored to address these bottlenecks. We start by presenting three representative forms of ELAA systems: single-base station ELAA systems, coordinated distributed antenna systems, and ELAA systems integrated with emerging technologies. For each form, we review the associated distributed SP algorithms in the literature. Additionally, we outline several important future research directions that are essential for improving the performance and practicality of ELAA systems.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 2","pages":"304-330"},"PeriodicalIF":8.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10883023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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