基于OAMP算法辅助稀疏连接神经网络的MIMO信号检测  

MIMO Signal Detection Based on OAMP Algorithm Assisted Sparsely Connected Neural Networks

作　　者：申滨[1] 阳建 涂媛媛 SHEN Bin;YANG Jian;TU Yuanyuan(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《信号处理》2023年第5期910-918,共9页Journal of Signal Processing

基　　金：国家自然科学基金(62071078)。

摘　　要：最大似然检测(Maximum likelihood detection,ML)是传统多输入多输出(Multi-input Multi-output,MIMO)信号检测中的最优算法,但是受到天线数量、收发天线比例以及调制信号的约束,致使其仅适用于天线数量少、天线比例较低且调制信号阶数较低的场景。作为一种新型的解决方案,目前基于深度学习(DL)的信号检测算法得到了广泛关注,但同样存在收发天线规模相近时检测性能恶化问题。该文将正交近似消息传递(OAMP)算法与稀疏连接神经网络(ScNet)结合成为可训练的网络结构,提出一种新的适用于MIMO系统上行链路的信号检测算法,称作ScNet-OAMP。该算法通过神经网络提供精确的信号传输参数初始解,改善OAMP过程的线性估计和非线性估计,由此增强其降噪能力,达到提高检测精度的目的,相比于ScNet和OAMP,其能够在同等实验参数下获得最佳检测性能。实验结果表明,此算法适用于QPSK、4QAM及16QAM等不同调制信号,能够处理不同比例收发天线及数量规模的系统配置,尤其是在收发天线数量相近的情况下亦能表现出较好的性能,并且在10-3误码率上有至少0.5 dB,甚至2.2 dB以上的性能增益。Maximum likelihood detection(ML)is the optimal algorithm for traditional Multi-input Multi-output(MIMO)signal detection.However,it is constrained by the number of antennas,the ratio of transmitting and receiving antennas,and the modulation scheme,making it only appropriate for scenarios where the number of antennas is relatively small,the ratio of transmitting and receiving antennas is low,and the signal is of low modulation order.As a novel solution,deep learning(DL)-based signal detection algorithms have received much attention but also suffer from deterioration in detection performance when the transmitting and receiving antenna sizes are mutually similar.This paper combines the orthogonal approximate message passing(OAMP)algorithm with a sparsely connected neural network(ScNet)into a trainable network structure and proposes a new signal detection algorithm for the uplink of MIMO systems,called ScNet-OAMP.The algorithm provides an accurate initial solution of the signal transmission parameters through a neural network,which improves the linear and non-linear estimation of the OAMP process,therefore enhancing its noise reduction capability and achieving an improved detection accuracy,with the best detection performance compared to ScNet and OAMP with the same experimental parameters.The experimental results show that the algorithm is suitable for different modulation signals such as QPSK,4QAM,and 16QAM and can handle system configurations with different ratios and numbers of transceiver antennas,especially when the number of transceiver antennas is nearly the same,with a performance gain of at least 0.5 dB,and even 2.2 dB or more,in the 10-3 bit error rate.

关 键 词：检测 深度学习 正交近似消息传递 初始解 稀疏连接 

分 类 号：TN929.5[电子电信—通信与信息系统]