Channel information compression feedback method based on deep cyclic neural network

Abstract

The invention discloses a channel information compression feedback method based on a deep cyclic neural network. The method comprises the following steps: firstly transforming a channel matrix into anangle-delay domain by discrete Fourier transform, then retaining columns containing non-zero elements in the channel matrix, splitting the complex matrixes composed of the columns into two-channel real matrixes, firstly using the convolutional network to extract frequency domain features of the channel and then compressing the extracted frequency domain features through a fully connected neural network and a long-short-time memory network by the user side, transmitting the compressing signal to the base station through a system feedback link, and decompressing the signal by using the fully connected neural network and the long-short-time memory network, and recovering and reconstructing the channel matrix using a multi-layer convolutional neural network by the base station. By introducinga cyclic neural network, the method effectively compresses the temporal redundancy of the channel, and improves the compression ratio of the channel information under the premise of ensuring higher performance.

Description

technical field [0001] The invention belongs to the technical field of compressed sensing and channel information recovery, and relates to a channel information compression feedback method, in particular to a channel information compression feedback method based on a deep cycle neural network. Background technique [0002] Millimeter-wave multiple-input multiple-output (MIMO) is one of the key technologies of the fifth-generation mobile communication, which can ensure that the communication system has ultra-high system capacity, energy efficiency and anti-interference ability. [0003] In millimeter-wave MIMO, the number of antennas on the base station side is usually very large, which leads to downlink pilot information and uplink channel state information (CSI) feedback occupying a large amount of resources. Compressed sensing technology is based on the assumption of channel sparsity, which can compress channel information to a greater extent and restore the original chann...

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Problems solved by technology

[0004] In order to solve the problem that the existing method does not consider time redundancy in the channel compression feedback problem and the feedback accuracy is low, the present invention proposes a channel information compression feedback method based on a deep recurrent neural network, introduces a recurrent neural network, and utilizes a recurrent neural network The memory characteristics of the network better compress the redundancy in timing

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