关键词: 频分双工, 大规模MIMO, 信道状态信息, 深度学习, 深度可分离卷积神经网络, 残差网络

Abstract:

Aiming at the problems of high complexity, low precision and high overhead of the channel state information(CSI) feedback method in frequency division duplexing massive multiple input multiple output(MIMO) communication system, this paper proposes a low complexity CSI feedback method based on deep learning. This method constructs a network structure from the encoder of user equipment to the decoder of base station in an end-to-end way. The codec uses a continuous average pooling layers and up sampling layers to complete the dimensionality reduction and dimensionality increase of the feature map, and introduces a depthwise separable convolutional neural network to reduce the amount of network parameters. In the decoder part, this paper uses the residual network to construct continuous residual blocks with large convolution kernel to approximate the original CSI matrix. Simulation results show that, compared with the existing representative methods, the method proposed in this paper has 2dB~5dB improvement in normalized mean square error, and 2%~5% improvement in cosine similarity, and it has better performance in time complexity and space complexity.

Key words: frequency division duplexing, massive MIMO(Multiple-Input Multiple-Output), channel state information, deep learning, depthwise sparable convolution neural network, residual network