无线电工程2026，Vol.56Issue(3)：426-435,10.DOI:10.3969/j.issn.1003-3106.2026.03.005
基于用户数估计的MIMO无源多址接收优化
MIMO Passive Multiple Access Reception Optimization Based on User Number Estimation
摘要
Abstract
To solve the problems of severe interference between user signals and low decoding success rate in Passive Multiple Access(UMA)systems under high-density user scenarios,it is necessary to improve signal separation and decoding performance of the system in such scenarios.A multi-level iterative reception framework is constructed based on Multiple-Input Multiple-Output(MIMO)technology,and performance optimization is achieved by combining user number estimation,multi-beam interpretation scheduling,and iterative decoding technologies.A beam user number estimation model based on deep learning is designed to dynamically schedule low-conflict beams for priority decoding,while eliminating interference from decoded users through cross-beam Successive Interference Cancellation(SIC).Simulation results show that the beam decoding success rate in high-density user scenarios is improved by approximately 28.09%compared with the traditional Compressed Sensing-Orthogonal Matching Pursuit(CS-OMP)algorithm,by about 18.3%and 10.4%compared with the Approximate Message Passing(AMP)and Sparse Low-Rank Matrix Algorithm(SPARC)schemes,respectively,and remains stable when the number of active users reaches 600.Through the closed-loop optimization of spatial resource allocation and cooperative interference suppression,this multi-level iterative reception framework significantly improves the capacity and robustness of the system in massive user scenarios,providing an innovative solution for 6G ultra-dense internet of things access.关键词
无源多址接入/多级迭代接收/空域滤波/深度学习/多波束解译调度/用户密集场景/6G物联网Key words
passive multiple access/multi-level iterative reception/spatial domain filtering/deep learning/multi-beam interpretation scheduling/user-intensive scenarios/6G internet of things分类
信息技术与安全科学引用本文复制引用
周国正,边东明,张更新..基于用户数估计的MIMO无源多址接收优化[J].无线电工程,2026,56(3):426-435,10.基金项目
国家自然科学基金区域创新发展联合基金重点项目(U21A20450) National Natural Science Foundation of China Regional Innovation and Development Joint Fund Key Project(U21A20450) （U21A20450）
