Channel state information feedback method based on deep learning and entropy coding

Abstract

The invention discloses a channel state information feedback method based on deep learning and entropy coding, and the method comprises the steps: carrying out the preprocessing of a channel matrix of MIMO channel state information at a user side, selecting key matrix elements to reduce the calculation amount, and obtaining a channel matrix H which is actually used for feedback; constructing a model combining a deep learning feature encoder and entropy encoding at a user side, and encoding a channel matrix H into a binary bit stream; at a base station end, constructing a model combining a deep learning feature decoder and entropy decoding, and reconstructing an original channel matrix estimated value from a binary bit stream; training the model to obtain model parameters and an output reconstruction value of a reconstructed channel matrix, and finally applying the trained model based on deep learning and entropy coding to compressed sensing and reconstruction of channel information. According to the invention, the large-scale MIMO channel state information feedback overhead can be reduced.

Description

Technical field [0001] The present invention relates to a large-scale MIMO channel state information feedback method based on deep learning and entropy coding. Background technique [0002] Massive Multiple-Input Multiple-output technology is considered to be key technologies for 5G and after 6G communication systems. By using multiple emission and multiple receiving antennas, the MIMO system can significantly increase capacity without expanding additional bandwidth. Based on the potential advantages of the above-mentioned large-scale MIMO system, based on the basis of the base station, the base station can accurately know the channel status information, and to eliminate interference between multi-user by precoding, however, for FREQUENCY DIVSION DUPEXITY MIMO system The uplink and downlink work on different frequency points, so the downlink channel status information is obtained by the user, and the return base station is transmitted through the feedback link. Considering that t...

AI Technical Summary

Problems solved by technology

Then in actual communication, in order to further improve the accuracy of channel information recovery and reduce the feedback overhead, it is necessary to consider the encoding and decoding process at the transmitting and receiving ends, and there are very few literatures related to this aspect.

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