基于人工智能的大规模天线信道状态信息反馈研究

Study of channel state information feedback based on artificial intelligence

信道状态信息(channel state information,CSI)的精确获取是大规模天线发挥效能的关键。在现有的通信系统中,上下行链路互易性不理想时,基于码本进行下行链路的CSI反馈。随着天线规模的增大,码本CSI反馈所需要的开销也越来越大。给出了基于人工智能(artificial intelligence,AI)的CSI反馈压缩方法,分析了基于AI的CSI反馈的标准化影响、通信流程与面临的挑战,提供了评估结果。评估结果表明,相对于基于频域基向量压缩的码本CSI反馈,基于AI的CSI反馈在相同的反馈精度下可以显著地降低反馈开销。

Accurate acquisition of CSI(channel state information)is the key to the performance of massive MIMO.In current communication systems,when the reciprocity of uplink and downlink is not ideal,codebook-based CSI feedback is used for downlink CSI acquisition.With the increase of antenna scale,codebook-based CSI feedback needs more and more overhead.The CSI feedback compression method based on AI(artificial intelligence)was pre-sented,and the standardization impact,communication process and challenges of CSI feedback based on AI were analyzed.Besides,evaluation results were provided.The evaluation results show that compared with codebook-based CSI feedback based on frequency domain basis vector compression,CSI feedback based on AI can significantly re-duce the feedback cost at the same feedback accuracy.