A low-complexity method for massive MIMO uplink transmission channel estimation

Abstract

The invention discloses a large-scale MIMO uplink transmission channel estimation method with low complexity. The method mainly comprises the following steps: a base station generates a large-scale beam set to cover the whole cell through beamforming, and all users synchronously send pilot frequency signals to the base station; after acquiring pilot frequency information, the base station constructs a beam-domain compressed channel estimation problem according to a pilot frequency matrix, and estimates a normalized angle delay domain channel response matrix through a way of weighting a sensingmatrix by using a priority vector according to a structural sparsity and energy concentration feature of the beam domain channel; and the estimation on the space-frequency domain channel response matrix is accomplished through a matrix multiplying way after obtain the normalized angle delay domain channel response matrix. The power leakage is reduced by using an over-complete discrete Fourier matrix, the sparsity of the angle delay domain is increased, the recovery precision of the compressed channel is improved, the retrieval frequency of the channel vector estimation process is lowered through the weight vector and the priority matrix, and the complexity is reduced.

Description

technical field [0001] The invention belongs to the communication field, and in particular relates to a low-complexity compressed channel estimation method for large-scale multiple-input multiple-output (Multiple-Input Multiple-Output, MIMO) uplink transmission under a frequency selective channel. Background technique [0002] In a massive MIMO system, the base station is configured to use a large-scale antenna array to serve multiple users at the same time. The use of massive MIMO technology can effectively reduce the interference between users, and greatly improve the spectrum utilization and power efficiency of the wireless communication system. For massive MIMO communication systems, channels in the beam domain inherently exhibit block sparsity due to the limited number of important scatterers in the propagation environment. Therefore, the compressed sensing algorithm can be used to reconstruct the channel response matrix. [0003] In order to fully obtain the performa...

AI Technical Summary

Problems solved by technology

But for large-scale user terminals, the use of orthogonal pilot sequences will make the pilot overhead unaffordable

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