Liu Qingli, Sun Jiaxu, Wang Peiling
Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Entropy (Basel). 2023 Jul 27;25(8):1131. doi: 10.3390/e25081131.
In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input-multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder-decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink's magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.
为了解决大规模多输入多输出系统中基站天线部署过多，导致下行链路信道状态信息反馈开销过高的问题，本文提出了一种基于深度学习的上行链路辅助信道反馈方法。该方法利用上行链路和下行链路的互易性，在基站使用上行链路信道状态信息来帮助用户反馈未知的下行链路信息，并对信道状态信息进行压缩和恢复。首先，建立一个编码器 - 解码器结构。编码器降低网络深度，并使用多分辨率卷积来提高信道状态信息提取的准确性，同时减少与用户设备相关的计算量。之后，对信道状态信息进行压缩以减少信道中的反馈开销。在解码器处，借助上行链路和下行链路的互易性，对上行链路幅度进行特征提取，并将下行链路信道状态信息整合到一个信道状态信息特征矩阵中，然后将其恢复到原始大小。仿真结果表明，与CSINet、CRNet、CLNet和DCRNet相比，在所有压缩情况下，室内重建精度平均提高了16.4%，室外重建精度平均提高了21.2%。
