Csi compression and feedback method based on deep learning

A CSI compression and feedback method based on deep learning, comprising:

• step S1, establishing and training a two-dimensional sequence-to-sequence structure neural network;
• sub-step S1.1, collecting, by a base station, CSI data from historical communication data as training data of the two-dimensional sequence-to-sequence structure neural network;
• sub-step S1.2, building, by the base station, the two-dimensional sequence-to-sequence structure neural network, and randomly initializing parameters of the two-dimensional sequence-to-sequence structure neural network, wherein the two-dimensional sequence-to-sequence structure neural network comprises an encoder and a decoder, the encoder comprises first cells and first sub-blocks, the decoder comprises second cells and second sub-blocks, the encoder comprises one two-dimensional recurrent neural network and one fully connected network in a cascade connection, and the decoder comprises one fully connected network and one two-dimensional recurrent neural network in a cascade connection;
• inputting, by the encoder, the first sub-blocks divided from CSI into the first cells, concatenating outputs of the first cells into a first matrix according to an order of the first cells, and compressing the first matrix into a first vector;
• and reshaping, by the decoder, the compressed first vector into a second matrix, dividing the second matrix into the second sub-blocks, input the second sub-blocks into the second cells, and concatenating outputs of the second cells according to an order of the second cells to obtain reconstructed CSI;
• and
• sub-step S1.3, training, by the base station, the two-dimensional sequence-to-sequence structure neural network in the sub-step S1.2 by using the collected CSI data, and continuously training the parameters of the two-dimensional sequence-to-sequence structure neural network by a gradient descent method until convergence;
• step S2, distributing, by the base station, the encoder of the two-dimensional sequence-to-sequence structure neural network trained in the sub-step S1.3 to a client;
• step S3, compressing, by the client, downlink CSI by using the encoder distributed in the step S2, and feeding the compressed downlink CSI back to the base station;
• and
• step S4, reconstructing, by the base station, the compressed downlink CSI fed back by the client in the step S3 by using the decoder of the two-dimensional sequence-to-sequence structure neural network trained in the sub-step S1.3 to obtain complete downlink CSI.