Efficient MIMO channel feedback method based on binary neural network and device

Abstract

The invention discloses an efficient MIMO channel feedback method based on a binary neural network and a device thereof. The method comprises the following steps: determining the dimension of a space-frequency domain downlink channel matrix of a user side channel according to the number of OFDM subcarriers in a communication system and the number of base station end antennas; inputting the space-frequency domain downlink channel matrix into an auto-encoder based on a binary neural network for compression to obtain a channel feature vector, and sending the channel feature vector from a user side to a base station side through an uplink; and decoding the received channel feature vector through a neural network-based self-decoder at the base station end to obtain a space-frequency domain downlink channel matrix. According to the scheme, low-overhead auto-encoder neural network deployment can be carried out on a resource-limited user side, and a more practical channel compression feedback scheme is realized.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a high-efficiency MIMO channel feedback method and device based on a binary neural network. Background technique [0002] The channel feedback accuracy of traditional codebook-based MIMO (Multiple-Input Multiple-Output, multiple-input multiple-output system) systems is limited, especially after the scale of MIMO is expanded to massive MIMO, its feedback accuracy will be further attenuated. [0003] The full-channel feedback or sub-full-channel feedback of the MIMO system needs to feed back a large amount of information, which brings unbearable feedback overhead. In addition, traditional compressed sensing technology is difficult to achieve a sufficiently low compression ratio and a sufficiently low compression information loss. [0004] The MIMO system channel compression feedback technology based on deep learning achieves lower compression information loss at a lower com...

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Problems solved by technology

[0003] The full-channel feedback or sub-full-channel feedback of the MIMO system requires a large amount of feedback information, which brings unbearable feedback overhead

In addition, traditional compressed sensing technology is difficult to achieve a sufficiently low compression ratio and a sufficiently low compression information loss

[0004] The MIMO system channel compression feedback technology based on deep learning achieves lower compression information loss at a lower compression rate by learning channel characteristics, but for resource-constrained user equipment, the existing autoencoder network Too bulky for both storage and computation

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