Ali Bagheri Bardi;Taher Yazdanpanah;Miloš Daković;Ljubiša Stanković
{"title":"Graph Fourier Transform Enhancement Through Envelope Extensions","authors":"Ali Bagheri Bardi;Taher Yazdanpanah;Miloš Daković;Ljubiša Stanković","doi":"10.1109/TSP.2025.3608810","DOIUrl":"10.1109/TSP.2025.3608810","url":null,"abstract":"Many real-world networks are characterized by directionality; however, the absence of an appropriate Fourier basis hinders the effective implementation of graph signal processing techniques. Inspired by discrete signal processing, where embedding a line digraph into a cycle digraph facilitates the powerful Discrete Fourier Transform (DFT) for signal analysis, addressing the structural complexities of general digraphs can help overcome the limitations of the Graph Fourier Transform (GFT) and unlock its potential. The DFT functions as a GFT for both cycle graphs and Cayley digraphs defined over the finite cyclic group <inline-formula><tex-math>$mathbb{Z}_{N}$</tex-math></inline-formula>. In this work, we present a systematic framework to identify a class of Cayley digraphs capable of encompassing a given directed graph. By embedding the original directed graph into such Cayley digraphs and employing envelope extensions that inherently support the GFT, we enable the use of the GFT associated with the extended structures for signal analysis. Among the candidate envelope extensions, optimal performance is achieved by selecting one that satisfies a set of pre-specified structural properties. The resulting GFT is numerically stable and provides a basis that approximates the eigenstructure of the adjacency matrix associated with the original digraph. It is shown that the envelope extensions possess a convolution product, with their GFT fulfilling the convolution theorem. Additionally, shift-invariant graph filters (systems) are described as the convolution operator, analogous to the classical case. This allows the utilization of systems for signal analysis.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3598-3613"},"PeriodicalIF":5.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073014","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}
{"title":"Robust Quickest Change Detection With Sampling Control","authors":"Yingze Hou;Hoda Bidkhori;Taposh Banerjee","doi":"10.1109/TSP.2025.3609067","DOIUrl":"10.1109/TSP.2025.3609067","url":null,"abstract":"The problem of quickest detection of a change in the distribution of a sequence of random variables is studied. The objective is to detect the change with the minimum possible delay, subject to constraints on the rate of false alarms and the cost of observations used in the decision-making process. The post-change distribution of the data is known only within a distribution family. It is shown that if the post-change family has a distribution that is least favorable in a well-defined sense, then a computationally efficient algorithm can be designed that uses an on-off observation control strategy to save the cost of observations. In addition, the algorithm can detect the change robustly while avoiding unnecessary false alarms. It is shown that the algorithm is also asymptotically robust optimal as the rate of false alarms goes to zero for every fixed constraint on the cost of observations. The algorithm’s effectiveness is validated on simulated data and real public health data.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3588-3597"},"PeriodicalIF":5.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072708","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}
{"title":"Multi-target Range, Doppler and Angle estimation in MIMO-FMCW Radar with Limited Measurements","authors":"Chandrashekhar Rai, Himali Singh, Arpan Chattopadhyay","doi":"10.1109/tsp.2025.3609460","DOIUrl":"https://doi.org/10.1109/tsp.2025.3609460","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"44 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072736","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}
Xiumei Deng, Jun Li, Kang Wei, Long Shi, Zehui Xiong, Ming Ding, Wen Chen, Shi Jin, H. Vincent Poor
{"title":"Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization","authors":"Xiumei Deng, Jun Li, Kang Wei, Long Shi, Zehui Xiong, Ming Ding, Wen Chen, Shi Jin, H. Vincent Poor","doi":"10.1109/tsp.2025.3608715","DOIUrl":"https://doi.org/10.1109/tsp.2025.3608715","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"53 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035855","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}
{"title":"Channel Estimation and Data Detection in DS-Spread Channels: A Unified Framework, Novel Algorithms, and Waveform Comparison","authors":"Niladri Halder, Chandra R. Murthy","doi":"10.1109/tsp.2025.3608021","DOIUrl":"https://doi.org/10.1109/tsp.2025.3608021","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"35 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035879","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}
Yonghui Chu;Wenlong Wang;Shixuan Liu;Zhiqiang Wei;Zai Yang
{"title":"Downlink-Uplink Collaborative Channel Estimation for TDD Massive MIMO Communications","authors":"Yonghui Chu;Wenlong Wang;Shixuan Liu;Zhiqiang Wei;Zai Yang","doi":"10.1109/TSP.2025.3607997","DOIUrl":"10.1109/TSP.2025.3607997","url":null,"abstract":"Channel estimation (CE) is a crucial component in massive multiple-input multiple-output (MIMO) communication systems, while existing CE methods require a large training overhead and suffer from limited estimation accuracy due to the excessively high number of antennas. In this paper, we focus on the CE problem for time-division duplex (TDD) massive MIMO systems, where downlink (DL) and uplink (UL) channels exhibit strong reciprocity. To fully exploit the channel reciprocity, we design a DL-UL collaborative channel sounding scheme that employs a limited number of transmit antennas on both sides to save training overhead. By integrating DL and UL channel measurements with different signal-to-noise ratios into two data-fitting terms, we formulate the CE problem as a downlink-uplink collaborative atomic norm minimization (DUCANM) problem and provide theoretical analysis to select the hyperparameters involved. A partially decoupled atomic norm minimization formulation is proposed to solve the DUCANM problem effectively. To further accelerate the computation of DUCANM, we propose a fast algorithm based on the alternating direction method of multipliers. Numerical simulations are provided that demonstrate the superiority of our proposed method in terms of CE accuracy, training overhead, and running time.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3614-3628"},"PeriodicalIF":5.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035877","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}