西安电子科技大学学报（自然科学版）2026，Vol.53Issue(2)：175-185,11.DOI:10.19665/j.issn1001-2400.20251114
基于深度学习的MIMO-OFDM盲源分离算法
Blind source separation algorithm based on deep learning for MIMO-OFDM systems
摘要
Abstract
This paper addresses blind separation of OFDM signals in multiple-input multiple-output(MIMO)systems under non-cooperative communication scenarios.To overcome the insufficient separation accuracy of traditional algorithms under phase ambiguity and emission angle variations,an end-to-end multichannel blind separation network based on time-domain beamforming is proposed.The network employs a two-stage architecture that integrates inter-channel and channel-specific features,and utilizes DPRNN to estimate beamforming filters for OFDM signal separation.To enhance robustness to variations in emission angles,a transform-average-concatenate(TAC)module is integrated between DPRNN blocks for information alignment and feature fusion.In addition,a customized loss function is designed to effectively resolve the phase inversion problem.Simulation results demonstrate that under 4QAM with a signal-to-noise ratio(SNR)of 14 dB,the proposed method reduces the bit error rate(BER)below 10-3,thus significantly outperforming traditional blind source separation algorithms and verifying the effectiveness of the network.Furthermore,its low computational complexity and end-to-end structure provide practical feasibility for future engineering applications.关键词
盲源分离/正交频分复用/多输入多输出/深度学习/相位反转Key words
blind source separation(BSS)/orthogonal frequency division multiplexing(OFDM)/multiple-input multiple-output(MIMO)/deep learning/phase reversal分类
信息技术与安全科学引用本文复制引用
付卫红,冯婧一,刘乃安..基于深度学习的MIMO-OFDM盲源分离算法[J].西安电子科技大学学报（自然科学版）,2026,53(2):175-185,11.基金项目
国家自然科学基金(62376204,62476208) （62376204,62476208）
