基于压缩感知的大规模MIMO分段信道反馈

Segmental Channel Feedback for Massive MIMO with Compressive Sensing

大规模多入多出技术(Multiple-Input Multiple-Output,MIMO)是未来5G无线通信的关键技术。在MIMO系统中,发送端的空时编码、接收端的信号检测都需要信道状态信息(Channel State Information,CSI),而大规模MIMO的信道反馈问题随着MIMO信道矩阵的尺寸越来越大,变得越来越具有挑战性。为此,在研究大规模MIMO系统中信道脉冲响应(Channel Impulse Response,CIR)反馈的基础上,提出了一种基于压缩感知的分段CIR反馈方案。应用该方案分段后的信道有着比原信道更好的稀疏性,基站可以利用压缩感知恢复分段后的经过高度压缩的CIR。仿真结果表明,所提出的方案可大幅度降低反馈误差,当压缩率为20%时,直接压缩方案已经失效,而所提出的方案表现却良好;当压缩率为50%时,所提出的方案能够获得高于直接压缩方案5 dB的SNR增益。

Massive Multiple-input Multiple-Output （MIMO） is becoming a key technology for future 5G wireless communications. In MIMO systems ,Channel State Information （CSI） is essential for both space-time coding at transmitters and signal detection at receivers. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of MIMO channel matrix. For this rea- son, on the basis of the study of Channel Impulse Response （CIR） feedback for massive MIMO systems, a segmented CIRs fee.Aback scheme based on compressive sensing has been proposed. Specifically, segmented channels are sparser than the original channel. Thus, the base station can recover the highly compressed segmented CIRs under the framework of compressive sensing. Simulation results show that the proposed scheme can reduce the feedback error compared with the direct CS-based scheme and that when compression ratio is 20%, the direct CS-based scheme falls to work since the feedback while the proposed scheme performs well;when compression ratio is 50%, the proposed scheme achieves a 5 dB SNR gain compared with the direct CS-based scheme.