Multi-user large-scale MIMO channel estimation method and device

Abstract

The invention relates to a multi-user large-scale MIMO channel estimation method and a multi-user large-scale MIMO channel estimation device, which aim at a Massive MIMO multi-user communication scene. The device comprises a user receiving signal module, a receiving signal covariance matrix smoothing module, a user arrival angle rapid estimation module, a beam forming technology estimation channelgain module and a channel state information matrix reconstruction module. According to the method, the angle of each scattering path of each user is accurately estimated by using a rapid channel covariance matrix smoothing technology and a rapid MUSIC spectrum search method, extremely low complexity is ensured, then channel gain on each scattering path is estimated by using a beam forming technology, and finally, a state information matrix is reconstructed. The MMSE channel estimator has very excellent channel estimation performance under the condition of full rank or low rank of the channel,exceeds the current optimal MMSE channel estimator, and is particularly suitable for the current 5G communication scene with very high requirements on time delay and transmission rate.

Description

technical field [0001] The invention belongs to the technical field of Massive multiple input multiple output (MIMO) of large-scale antennas, and in particular relates to a multi-user massive MIMO channel estimation method and device. Background technique [0002] Massive MIMO technology, namely Massive MIMO technology, is one of the fifth-generation wireless communication systems by significantly expanding the coverage area and allowing more users to access the mobile base-station (MBS). It is a key technology, which can greatly increase the channel transmission capacity and rate, which is extremely important for the three main application scenarios of 5G communication. In order to successfully complete the information transfer process between the target and the base station and to obtain subsequent accurate precoding design, the first task is to estimate channel state information (CSI, channel state information). For most 5G application scenarios with ultra-low latency re...

AI Technical Summary

Problems solved by technology

However, in a time-varying UAV environment, the update of the weight coefficients still requires a large computational overhead

The LSE-SMP (least-square estimation and sparse message passing) method based on the least squares estimation and sparse message passing algorithm obtains the sparse matrix related to the channel by designing a good message feedback mechanism, and uses the sparse iterative method to estimate the mmWave channel The problem is transformed into a sparse signal recovery problem, and the position of the non-zero element is recovered by means of the message passing algorithm. This method can obtain the unbiased estimate with the smallest variance, but the applicability is extremely low. The search for the sparse basis will greatly affect the performance of the algorithm, and The complexity required by the algorithm is still very high, and the complexity required for message update is extremely high

[0005] Currently, the main goal of CSI estimators is to balance computational complexity and estimation accuracy, which is a major challenge for massive MIMO systems

Existing methods can reduce the time complexity to a certain extent, but few methods can exceed the estimation accuracy of MMSE estimators

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