Channel Estimation Method for Massive MIMO System Based on Block Structure Adaptive Compression Sampling Matching Pursuit Algorithm

Abstract

The invention discloses a massive MIMO system channel estimation method based on a block-structure self-adaptive compression sampling matching pursuit algorithm. T Antennas transmit pilot information, at N R receiving by a single-antenna user end, including the following steps: S1. Calculate the pilot information measurement vector y received at each user receiving end, and establish compression for the pilot information transmission process according to the sparse consistency of the massive MIMO system channel Perceive the mathematical model and construct the perceptual matrix Φ; S2. Obtain the block structure perceptual matrix Ψ through the block structure transformation, and reconstruct the block sparse signal g through the reconstruction algorithm; S3. Reconstruct the sparse signal using the block structure adaptive compression sampling matching pursuit algorithm signal h. The invention utilizes the time-domain sparsity consistency of the channel of the massive MIMO system, uses a block structure self-adaptive compression sampling matching tracking algorithm to reconstruct the channel impulse response, and can estimate when the sparsity is unknown and can reduce the use of pilot frequency.

Description

technical field [0001] The invention relates to communication channel estimation, in particular to a massive MIMO system channel estimation method based on a block-structure self-adaptive compression sampling matching pursuit algorithm. Background technique [0002] With the development of wireless communication transmission technology in recent years, massive MIMO technology has become one of the key technologies of 5G. It can build multiple signal transmission channels between the transmitting antenna and the user end, making full use of space resources. Generally, massive MIMO systems configure dozens or even hundreds of antennas at the base station to form an antenna array, serving multiple users at the same time, and using spatial multiplexing and transmission diversity technologies to improve the information transmission rate and transmission reliability of the system and at the same time Improved spectrum utilization. Research on signal detection and correlation equa...

AI Technical Summary

Problems solved by technology

There are still some problems in the current compressed sensing algorithm for channel estimation in massive MIMO systems, such as the need to know in advance the channel sparsity that is difficult to obtain in practical applications, or the inflexibility in the selection of atoms, so that the number of pilots used in estimation is still large Wait

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