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

A technology of channel state information and deep learning, which is applied in the field of massive MIMO channel state information feedback to achieve feedback and improve the quality of channel reconstruction

Active Publication Date: 2021-07-09
SOUTHEAST UNIV
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  • Application Information

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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 neces

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

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Embodiment Construction

[0029] A channel state information feedback method based on depth learning and entropy encoding of the present invention includes:

[0030] Step 1, at the client, the channel matrix of the MIMO channel status information is pre-processed, select the key matrix element to reduce the amount of calculation, and obtain the actual channel matrix H for feedback;

[0031] Step 2, in the client, construct a model including depth learning feature encoder and entropy encoding, encoding the channel matrix H is binary bitstream;

[0032] Step 3, in the base station, construct a model including the depth learning feature decoder and entropy decoding, and rebuilt the original channel matrix estimate from the binary bitstream obtained from step 2.

[0033] Step 4, for the combined model of step 2 and step 3, in the training process, while optimizing the entropy and reconstruction of the entropy encoder output during the training process, obtaining the ionized compression ratio and the recovery ...

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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...

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Application Information

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IPC IPC(8): H04L25/02H04B7/0413
CPCH04L25/0242H04L25/0256H04B7/0413Y02D30/70
Inventor 郑添月凌泰炀姚志伟田佳辰伍诗语郑怀瑾王闻今李潇金石
Owner SOUTHEAST UNIV
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