Feedback Reconstruction Algorithm for Large Scale MIMO Channels Based on Compressed Sensing

Abstract

The invention discloses a large-scale MIMO channel feedback reconstruction algorithm based on compressed sensing, including such steps as setting up system model, installing multiple antennas at basestation, serving multiple users at the same time, receiving each user with single antenna, uniformly linearly arranging antennas at base station and obtaining channel matrix at receiving end by channel estimation; 2, a channel state information compression step, vectorizing the channel matrix, a vector is obtained, the vector is compressed by the observation matrix to obtain the observation vector, and the observation vector (img file = 'DEST_PATH_IMAGE001. TIF' wi= '10' he= '14' / ) is sent to the base station through the feedback link. S3, the channel state information reconstruction step: after receiving the observed value vector, the base station performs numerical initialization and cyclic iteration, and finally obtains the reconstructed signal. The invention adopts the generalized orthogonal matching pursuit algorithm as the channel feedback reconstruction algorithm, which reduces the iterative times, not only effectively improves the reconstruction accuracy of the channel state information, but also shortens the reconstruction time.

Description

technical field [0001] The invention relates to a reconstruction algorithm, in particular to a large-scale MIMO channel feedback reconstruction algorithm based on compressed sensing, and belongs to the technical field of wireless communication. Background technique [0002] The fifth-generation mobile communication system (5G) has much higher spectrum utilization and energy efficiency than 4G, among which massive MIMO (Multiple-Input Multiple-Output) technology, as a key technology of 5G, has attracted widespread attention. The main feature of massive MIMO technology is that the base station uses large-scale antenna array technology and works in a multi-user scenario. The application of massive MIMO technology has significantly improved the system capacity, energy efficiency and system robustness. At the same time, massive MIMO technology brings many challenges while bringing the above advantages. In a massive MIMO system, the base station also needs to rely on accurate cha...

AI Technical Summary

Problems solved by technology

[0006] To sum up, most of the aforementioned articles use Orthogonal Matching Pursuit (OMP) as the compression reconstruction algorithm. OMP is widely used because of its low computational complexity, but each iteration of the OMP algorithm only takes An atom to update the support set will greatly increase the running time when the sparsity is low, and the OMP algorithm may not even be applicable when the amount of data is large

Images