The channel state information(CS1)of FDD downlink in massive multiple input multiple output(MIMO)systems is studied for feedback and reconstruction.The traditional CSI feedback and reconstruction algorithm based on compressed sensing has the problems of high computational complexity and strict requirement for channel sparsity,and the existing CSI feedback and reconstruction algorithms based on deep learning are insufficient in performance.For these reasons,a massive MIMO channel state information feedback and reconstruction algorithm based on joint attention mechanism neural network is proposed,and a new SE-CsiNet neural network model based on auto-encoder neural network architecture is proposed.The squeeze-and-excitation attention mechanism algorithm is introduced into the decoder network to improve the feature extraction ability of the neural network effectively.Experimental results show that the proposed algorithm has better performance than traditional CSI feedback and reconstruction algorithms and existing deep learning algorithms.
Massive MIMOCSI feedback and reconstructionAutoencoder neural networkAttention mechanism
NETL
NSTL
万方数据
