电讯技术2026，Vol.66Issue(2)：191-201,11.DOI:10.20079/j.issn.1001-893x.241018001
基于Kaczmarz-Net深度网络的大规模MIMO信号检测方法
Signal Detection Method Based on Kaczmarz-Net Deep Network for Massive MIMO Systems
摘要
Abstract
Benefiting from the channel hardening phenomenon and high-dimensional asymptotic properties,traditional linear signal detection algorithms can obtain excellent detection performance in massive multiple-input-multiple-output(MIMO)systems,but the heavy computational burden of the inverse of high-dimensional matrices leads to difficulties in practical applications.With the benefit of signal detection domain knowledge and deep learning techniques,a Kaczmarz-Net deep network(Kaczmarz-Net)massive MIMO uplink signal detection method is proposed.First,the default descending Kaczmarz detection algorithm with optimal comprehensive performance is implemented to design the deep network structure,and the iterative operation process of the algorithm is mapped into a deep network.Second,by combining the unique cyclic iterative update characteristics of the Kaczmarz algorithm,learnable parameters are introduced and the internal update structure of the algorithm is improved.In general,the simplified log-likelihood ratio is used to calculate the soft information,and the deep network is introduced into the soft judgment to improve detection accuracy.Experimental results demonstrate that under hard-decision detection conditions,the proposed Kaczmarz-Net achieves a performance gain of 1 dB compared to the minimum mean square error(MMSE)algorithm when the antenna configuration is 64×64 and the bit error rate(BER)is 10-2,while requiring only computational overhead quadratic in the number of transmit antennas.Under soft-decision conditions,the Kaczmarz-Net exhibits BER performance comparable to the MMSE soft detection algorithm.关键词
大规模MIMO/信号检测/深度学习/软输出/Kaczmarz算法Key words
massive MIMO/signal detection/deep learning/soft-output/Kaczmarz algorithm分类
信息技术与安全科学引用本文复制引用
金龙康,王辩铮,申滨..基于Kaczmarz-Net深度网络的大规模MIMO信号检测方法[J].电讯技术,2026,66(2):191-201,11.基金项目
国家自然科学基金资助项目(U23A20279) （U23A20279）
