Local clustering sparse channel estimation method based on bsl0

Abstract

The invention discloses a method utilizing a compressed sensing technology to estimate a local clustering sparse channel in an MIMO communication system. Sparsity of signals is utilized, original signals are efficiently restored based on a compressed sensing theory, and meanwhile the performance of the channel is guaranteed. The method for estimating the local clustering sparse channel can excellently detect the channel which is locally sparse and of a clustering structure, the channel can be relatively accurately estimated, reconstruction errors are greatly reduced, and signal restoring speed is improved. According to the method for estimating the local clustering sparse channel, accuracy of the algorithm is high, errors are fewer, performance in the process of estimating the channel is good, the original signals can be excellently restored, and estimating efficiency of the channel is effectively improved. Due to the fact that noise interference is unavoidable in the actual channel, for the local clustering sparse channel, in the fading environment where noise exists, the local clustering sparse channel is estimated, and the estimation result is real and accurate.

Description

technical field [0001] The invention belongs to the technical field of channel estimation in wireless communication, and in particular relates to a local clustering sparse channel estimation method. Background technique [0002] Channel estimation is an important research direction in wireless communication, and the quality of channel estimation directly affects the performance of coherent demodulation. In addition, adaptive modulation and multi-user scheduling of the wireless communication system require the receiving end of the system to know Channel State Information (CSI, Channel State Information). In MIMO channel transmission, the transmitted signal is often inevitably reflected, diffracted, and scattered by buildings, vehicles, etc., which causes frequency selective fading, which in turn leads to distortion of the signal at the receiving end. The schematic diagram of the MIMO channel is as follows figure 2 shown. Therefore, how to design a wireless communication sys...

AI Technical Summary

Problems solved by technology

In the previous signal estimation algorithms, the channel is simply assumed to be sparse, ignoring the clustering structure characteristics of the actual channel, so the effect of signal reconstruction is not accurate

Images