{"title":"Randomized Iterative Algorithms for Distributed Massive MIMO Detection","authors":"Zheng Wang;Cunhua Pan;Yongming Huang;Shi Jin;Giuseppe Caire","doi":"10.1109/TSP.2025.3558930","DOIUrl":null,"url":null,"abstract":"Distributed detection over decentralized baseband architectures has emerged as an important problem in the uplink massive MIMO systems. In this paper, the classic Kaczmarz method is fully investigated to facilitate the distributed detection for massive MIMO. First of all, a more general iteration performance result about the traditional randomized block Kaczmarz (RBK) method is derived, which paves the way for conceiving the conditional randomized block Kaczmarz (CRBK) algorithm. By customizing RBK with the concept of conditional sampling, CRBK achieves faster convergence and smaller error bound than RBK. To further exploit the potential of conditional sampling, multi-step conditional randomized block Kaczmarz (MCRBK) algorithm is proposed, which can be readily adopted as a flexible, scalable, low-complexity distributed detection scheme to suit various decentralized baseband architectures in massive MIMO. Moreover, to eliminate the convergence error bound of MCRBK, a novel dynamic step-size mechanism is proposed for the iteration update of MCRBK. Theoretical demonstration shows that the proposed distributed MCRBK detection with the optimized dynamic step-size not only converges exponentially to the solution of linear detection schemes but also enjoys the global convergence to well suit different practical scenarios of massive MIMO.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2304-2319"},"PeriodicalIF":4.6000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10959082/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Distributed detection over decentralized baseband architectures has emerged as an important problem in the uplink massive MIMO systems. In this paper, the classic Kaczmarz method is fully investigated to facilitate the distributed detection for massive MIMO. First of all, a more general iteration performance result about the traditional randomized block Kaczmarz (RBK) method is derived, which paves the way for conceiving the conditional randomized block Kaczmarz (CRBK) algorithm. By customizing RBK with the concept of conditional sampling, CRBK achieves faster convergence and smaller error bound than RBK. To further exploit the potential of conditional sampling, multi-step conditional randomized block Kaczmarz (MCRBK) algorithm is proposed, which can be readily adopted as a flexible, scalable, low-complexity distributed detection scheme to suit various decentralized baseband architectures in massive MIMO. Moreover, to eliminate the convergence error bound of MCRBK, a novel dynamic step-size mechanism is proposed for the iteration update of MCRBK. Theoretical demonstration shows that the proposed distributed MCRBK detection with the optimized dynamic step-size not only converges exponentially to the solution of linear detection schemes but also enjoys the global convergence to well suit different practical scenarios of massive MIMO.
期刊介绍:
The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.