{"title":"联合统计掩码学习和无支持先验分布估计","authors":"Mahdi Shamsi;Farokh Marvasti","doi":"10.1109/TSIPN.2025.3599781","DOIUrl":null,"url":null,"abstract":"This paper addresses the problem of distributed estimation under partial observability, where nodes mustcollaboratively process masked or incomplete measurements to infer a global target vector. Such masking arises from sensing limitations, communication constraints, or privacy requirements. We propose a novel framework for <italic>distributed masked information learning</i>, extending the Diffusion Least Mean Squares (DLMS) algorithm to operate under node-specific observation masks. To enable effective cooperation, we develop a signal-flow-inspired combination strategy and a thresholding-based algorithm for support inference. This allows each node to identify observable components of the target signal and adaptively control the diffusion of local estimates. We analyze the convergence of the proposed method in terms of mean and energy, and derive conditions for optimal threshold selection based on mask estimation error. Simulation results across both time- and transform-domain sparsity scenarios show that our method achieves a 30-40 dB improvement in mean square deviation over standard DLMS, matching the performance of fully observable settings under realistic observability ratios. These results underscore the potential of mask-aware adaptation for robust and scalable signal processing over networks.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1127-1137"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint Statistical Mask Learning and Distributed Estimation Without Support Priors\",\"authors\":\"Mahdi Shamsi;Farokh Marvasti\",\"doi\":\"10.1109/TSIPN.2025.3599781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper addresses the problem of distributed estimation under partial observability, where nodes mustcollaboratively process masked or incomplete measurements to infer a global target vector. Such masking arises from sensing limitations, communication constraints, or privacy requirements. We propose a novel framework for <italic>distributed masked information learning</i>, extending the Diffusion Least Mean Squares (DLMS) algorithm to operate under node-specific observation masks. To enable effective cooperation, we develop a signal-flow-inspired combination strategy and a thresholding-based algorithm for support inference. This allows each node to identify observable components of the target signal and adaptively control the diffusion of local estimates. We analyze the convergence of the proposed method in terms of mean and energy, and derive conditions for optimal threshold selection based on mask estimation error. Simulation results across both time- and transform-domain sparsity scenarios show that our method achieves a 30-40 dB improvement in mean square deviation over standard DLMS, matching the performance of fully observable settings under realistic observability ratios. These results underscore the potential of mask-aware adaptation for robust and scalable signal processing over networks.\",\"PeriodicalId\":56268,\"journal\":{\"name\":\"IEEE Transactions on Signal and Information Processing over Networks\",\"volume\":\"11 \",\"pages\":\"1127-1137\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Signal and Information Processing over Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11127002/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11127002/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Joint Statistical Mask Learning and Distributed Estimation Without Support Priors
This paper addresses the problem of distributed estimation under partial observability, where nodes mustcollaboratively process masked or incomplete measurements to infer a global target vector. Such masking arises from sensing limitations, communication constraints, or privacy requirements. We propose a novel framework for distributed masked information learning, extending the Diffusion Least Mean Squares (DLMS) algorithm to operate under node-specific observation masks. To enable effective cooperation, we develop a signal-flow-inspired combination strategy and a thresholding-based algorithm for support inference. This allows each node to identify observable components of the target signal and adaptively control the diffusion of local estimates. We analyze the convergence of the proposed method in terms of mean and energy, and derive conditions for optimal threshold selection based on mask estimation error. Simulation results across both time- and transform-domain sparsity scenarios show that our method achieves a 30-40 dB improvement in mean square deviation over standard DLMS, matching the performance of fully observable settings under realistic observability ratios. These results underscore the potential of mask-aware adaptation for robust and scalable signal processing over networks.
期刊介绍:
The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.