MIMO CSI反馈中的深度神经递归网络时空表示

在多输入多输出（MIMO）系统中，至关重要的是利用发射机处的可用信道状态信息（CSI）进行预编码，以提高频分双工（FDD）网络的性能。主要挑战之一是在大规模MIMO系统中压缩CSI反馈传输中的大量CSI。..

Spatio-Temporal Representation with Deep Neural Recurrent Network in MIMO CSI Feedback

In multiple-input multiple-output (MIMO) systems, it is crucial of utilizing the available channel state information (CSI) at the transmitter for precoding to improve the performance of frequency division duplex (FDD) networks. One of the mainchallenges is to compress a large amount of CSI in CSI feedback transmission in massive MIMO systems.In this paper, we propose a deep learning (DL)-based approach that uses a deep recurrent neural network (RNN) to learn temporal correlation and adopts depthwise separable convolution to shrink the model. The feature extraction module is also elaborately devised by studyingdecoupled spatio-temporal feature representations in different structures. Experimental results demonstrate that the proposed approach outperforms existing DL-based methods in terms of recovery quality and accuracy, which can also achieve remarkable robustness at low compression ratio (CR).