Quantization and inverse quantization method in large-scale MIMO channel state information feedback

Abstract

The invention discloses a quantization and inverse quantization method in large-scale MIMO channel state information feedback. The method comprises the following steps: firstly, acquiring a channel matrix at a user side, and performing two-dimensional DFT on the channel matrix, so that the channel matrix in a space-frequency domain is transformed into a channel matrix sparse in an angle-time delaydomain; secondly, constructing a model Quantized-CsiNet which is subjected to quantized channel feedback and reconstruction; thirdly, training the quantized channel feedback and reconstruction model;secondly, performing two-dimensional inverse DFT on the output reconstructed channel matrix, and recovering a channel matrix reconstruction value of an original space-frequency domain; and finally, applying the trained Quantized-CsiNet model to channel state information feedback in each actual scene, and reconstructing an original channel matrix. According to the method, the quantization module and the inverse quantization module are added into the CsiNet, so that the actually transmissible channel state information bit stream can be obtained, the feedback overhead of large-scale MIMO channelinformation is reduced, the reconstruction precision is improved, and particularly, the method has excellent robustness for quantization errors.

Description

technical field [0001] The invention relates to a quantization and inverse quantization method in massive MIMO channel state information feedback, and belongs to the technical field of communication. Background technique [0002] Massive Multiple-Input Multiple-Output (Massive MIMO) system is widely considered as a main technology of 5G wireless communication system. This system can greatly reduce multi-user interference by configuring hundreds or even thousands of antennas for the base station to form an antenna array, thereby simultaneously serving multiple users on the same time-frequency resource block and providing double-increased cell throughput. . However, the above potential benefits are mainly obtained by exploiting the CSI in the base station. Although Time-Division Duplexing (TDD) technology can obtain CSI from the uplink, it requires a complicated calibration process, while Frequency-Division Duplexing (FDD) technology completely needs to obtain CSI through fe...

AI Technical Summary

Problems solved by technology

Therefore, before transmission, the measurement matrix obtained by compressed sensing needs to be quantized and coded, but also introduces quantization errors

Images