Large-scale MIMO channel state information feedback method based on deep learning

A channel state information and deep learning technology, which is applied in the field of massive MIMO channel state information feedback, can solve the problem of high channel state information feedback overhead, achieve the effect of preserving beamforming gain, realizing feedback, and improving channel reconstruction quality

Active Publication Date: 2018-08-10
SOUTHEAST UNIV
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Problems solved by technology

[0004] Purpose of the invention: The present invention proposes a large-scale MIMO channel state information feedback method that can quickly and accurately reconstruct channel state information from low-compression feedback information, and solves the problem of large channel state information feedback overhead in large-scale MIMO systems

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  • Large-scale MIMO channel state information feedback method based on deep learning
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Embodiment Construction

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and a COST 2100 MIMO channel.

[0026] A large-scale MIMO channel state information feedback method based on deep learning. Through the data-driven encoder-decoder architecture, the encoder is used at the user end to compress and encode the channel state information into low-dimensional codewords, which are passed through the feedback link. Send it to the decoder at the base station and reconstruct the channel state information, reduce the channel state information feedback overhead, and at the same time improve the quality and speed of channel reconstruction, specifically including the following steps:

[0027] (1) In the downlink of a MIMO system, the base station uses N t = 32 transmit antennas, the user end uses a single receive antenna, the MIMO system adopts OFDM carrier modulation, using subcarriers. Using the COST 2100 model according to the above condi...

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Abstract

The invention discloses a large-scale MIMO channel state information feedback method based on deep learning. The method comprises the following steps: firstly, carrying out two-dimensional discrete Fourier transform (DFT) on a channel matrix H-wave of MIMO channel state information in a spatial frequency domain on a user side, so that a channel matrix H which is sparse in an angle delay domain isobtained; secondly, constructing a model CsiNet comprising a coder and a decoder, wherein the coder belongs to the user side and is used for coding the channel matrix H into codons with a lower dimension, and the decoder belongs to a base station side and is used for reconstructing an original channel matrix estimation value H-arrow from the codons; thirdly, training the model CsiNet to obtain model parameters; fourthly, carrying out two-dimensional inverse DFT on a reconstructed channel matrix H-arrow which is output by the CsiNet, so that a reconstructed value of the original channel matrixH-wave in the spatial frequency domain is recovered; and finally, using the trained model CsiNet for compressed sensing and reconstruction of channel information. The method provided by the inventionhas the advantages that large-scale MIMO channel state information feedback expenditures can be reduced, and an extremely high channel reconstruction quality and an extremely high channel reconstruction speed can be achieved.

Description

technical field [0001] The invention relates to a massive MIMO channel state information feedback method based on deep learning. Background technique [0002] The massive MIMO (multiple-input multiple-output) system is considered to be one of the key technologies of 5G wireless communication. This technology forms multiple independent channels in the space domain by configuring a large number of antennas at the base station, thereby greatly increasing the wireless communication system. throughput. Based on the above potential advantages of the massive MIMO system, it is based on the fact that the base station can accurately obtain the channel state information, and thus eliminate the interference between multiple users through precoding. However, for the FDD (frequency division duplexity) MIMO system , the uplink and downlink work on different frequency points, so the downlink channel state information is obtained by the user end and sent back to the base station through th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/0417H04B7/0456H04B7/06
CPCH04B7/0417H04B7/0456H04B7/066H04B7/0663
Inventor 金石王天奇韩瑜温朝凯
Owner SOUTHEAST UNIV
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