Pub Date : 2026-01-21DOI: 10.1109/tsp.2026.3656569
Saidur R. Pavel, Yimin D. Zhang, Shunqiao Sun
{"title":"2D DOA Estimation of Coherent Signals Exploiting Forward-Backward Covariance Tensor","authors":"Saidur R. Pavel, Yimin D. Zhang, Shunqiao Sun","doi":"10.1109/tsp.2026.3656569","DOIUrl":"https://doi.org/10.1109/tsp.2026.3656569","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"274 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042759","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}
Pub Date : 2026-01-21DOI: 10.1109/tsp.2026.3656332
Hansi Abeynanda, Chathuranga Weeraddana, Carlo Fischione
{"title":"On the Characteristics of the Conjugate Function Enabling Effective Dual Decomposition Methods","authors":"Hansi Abeynanda, Chathuranga Weeraddana, Carlo Fischione","doi":"10.1109/tsp.2026.3656332","DOIUrl":"https://doi.org/10.1109/tsp.2026.3656332","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"85 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042760","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}
Pub Date : 2026-01-21DOI: 10.1109/tsp.2026.3656662
Chengxi Li, Ming Xiao, Mikael Skoglund
{"title":"Biased Compression in Gradient Coding for Distributed Learning","authors":"Chengxi Li, Ming Xiao, Mikael Skoglund","doi":"10.1109/tsp.2026.3656662","DOIUrl":"https://doi.org/10.1109/tsp.2026.3656662","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"40 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042758","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}
Pub Date : 2026-01-21DOI: 10.1109/tsp.2026.3655921
Hongyu Han, Sheng Zhang, Hing Cheung So
{"title":"Privacy-Preserving Distributed Adaptive Filtering via Input Perturbation and Amplitude-Shifted Data Exchange over Networks","authors":"Hongyu Han, Sheng Zhang, Hing Cheung So","doi":"10.1109/tsp.2026.3655921","DOIUrl":"https://doi.org/10.1109/tsp.2026.3655921","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"117 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042757","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}
Pub Date : 2026-01-21DOI: 10.1109/tsp.2026.3656668
Chengen Liu, Victor M. Tenorio, Antonio G. Marques, Elvin Isufi
{"title":"Matched Topological Subspace Detector","authors":"Chengen Liu, Victor M. Tenorio, Antonio G. Marques, Elvin Isufi","doi":"10.1109/tsp.2026.3656668","DOIUrl":"https://doi.org/10.1109/tsp.2026.3656668","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"395 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042756","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}
Pub Date : 2026-01-20DOI: 10.1109/tsp.2026.3655839
Siddhartha Parupudi, Gourab Ghatak
{"title":"An Algorithm for Fixed Budget Best Arm Identification with Combinatorial Exploration","authors":"Siddhartha Parupudi, Gourab Ghatak","doi":"10.1109/tsp.2026.3655839","DOIUrl":"https://doi.org/10.1109/tsp.2026.3655839","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042772","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}
Pub Date : 2026-01-16DOI: 10.1109/tsp.2026.3654842
Xiaohuan Wu, Jin Qiu, Ji Sun, Wei Liu, Haiyang Zhang, Yonina C. Eldar
{"title":"Source Localization for Extremely Large-Scale Antenna Arrays under Spatial Non-Stationarity and Near-Field Effects","authors":"Xiaohuan Wu, Jin Qiu, Ji Sun, Wei Liu, Haiyang Zhang, Yonina C. Eldar","doi":"10.1109/tsp.2026.3654842","DOIUrl":"https://doi.org/10.1109/tsp.2026.3654842","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993254","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}
Pub Date : 2026-01-15DOI: 10.1109/tsp.2025.3648327
Xi Wang, Yang Liu, Xiaotong Zhao, Qingjiang Shi, Ye Yang
{"title":"On Stochastic Beamforming for Ergodic Sum-Rate Maximization in Cooperative Transmission Systems","authors":"Xi Wang, Yang Liu, Xiaotong Zhao, Qingjiang Shi, Ye Yang","doi":"10.1109/tsp.2025.3648327","DOIUrl":"https://doi.org/10.1109/tsp.2025.3648327","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"39 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972096","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}
Pub Date : 2026-01-14DOI: 10.1109/tsp.2026.3654026
Shaoxiu Wei, Mingchao Liang, Florian Meyer
{"title":"Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models","authors":"Shaoxiu Wei, Mingchao Liang, Florian Meyer","doi":"10.1109/tsp.2026.3654026","DOIUrl":"https://doi.org/10.1109/tsp.2026.3654026","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"60 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972166","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}
One-bit analog-to-digital converters (ADCs) offer a practical solution for reducing both cost and power consumption in massive multiple-input multiple-output (MIMO) systems. Nevertheless, the severe distortion induced by extremely coarse quantization significantly deteriorates the data detection performance. For 1-bit ADC, the conventional symmetric quantization may not necessarily be the optimal choice. This paper examines the impact of quantization thresholds on the performance of maximum likelihood (ML) data detection in one-bit massive MIMO systems, and proposes a revised ML (rML) detector with an iterative adaptive quantization design. Initially, the original ML detection problem, constrained by discrete constellations, is reformulated into the rML problem. By optimizing the quantization thresholds, the solution to the rML problem can be guided to converge to the true transmitted signal. Leveraging this characteristic, we propose the rML detector with iterative adaptive quantization design to progressively refine the quantization thresholds during data detection. We further present two implementation strategies for the rML detector: one iteration one quantization (1I1Q) and one iteration two quantization (1I2Q). Additionally, an rML-based joint channel estimation and data detection (JED) method is introduced, where the decoded data from 1I1Q is utilized to enhance the pilot data vectors, thereby refining the estimated channel and ultimately enhancing data detection performance. Finally, numerical results demonstrate that the proposed rML detector with iterative adaptive quantization design is both efficient and robust, significantly outperforming the state-of-the-art methods.
{"title":"Revised Maximum-Likelihood Detector With Quantization Design for One-Bit Massive MIMO Systems","authors":"Shumei Wei;Jin Xu;Xiaofeng Tao;Shixun Gong;Rui Meng","doi":"10.1109/TSP.2026.3653950","DOIUrl":"10.1109/TSP.2026.3653950","url":null,"abstract":"One-bit analog-to-digital converters (ADCs) offer a practical solution for reducing both cost and power consumption in massive multiple-input multiple-output (MIMO) systems. Nevertheless, the severe distortion induced by extremely coarse quantization significantly deteriorates the data detection performance. For 1-bit ADC, the conventional symmetric quantization may not necessarily be the optimal choice. This paper examines the impact of quantization thresholds on the performance of maximum likelihood (ML) data detection in one-bit massive MIMO systems, and proposes a revised ML (rML) detector with an iterative adaptive quantization design. Initially, the original ML detection problem, constrained by discrete constellations, is reformulated into the rML problem. By optimizing the quantization thresholds, the solution to the rML problem can be guided to converge to the true transmitted signal. Leveraging this characteristic, we propose the rML detector with iterative adaptive quantization design to progressively refine the quantization thresholds during data detection. We further present two implementation strategies for the rML detector: one iteration one quantization (1I1Q) and one iteration two quantization (1I2Q). Additionally, an rML-based joint channel estimation and data detection (JED) method is introduced, where the decoded data from 1I1Q is utilized to enhance the pilot data vectors, thereby refining the estimated channel and ultimately enhancing data detection performance. Finally, numerical results demonstrate that the proposed rML detector with iterative adaptive quantization design is both efficient and robust, significantly outperforming the state-of-the-art methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"409-422"},"PeriodicalIF":5.8,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961946","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}
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