Structured channel covariance estimation from limited samples for large antenna arrays

Author:

Yang Tianyu,Barzegar Khalilsarai Mahdi,Haghighatshoar Saeid,Caire GiuseppeORCID

Abstract

AbstractIn massive multiuser multiple antenna systems, the knowledge of the users’ channel covariance matrix is crucial for minimum mean square error channel estimation in the uplink and it plays an important role in several multiuser beamforming schemes in the downlink. Due to the large number of base station antennas, accurate covariance estimation is challenging especially in the case where the number of samples is limited and thus comparable to the channel vector dimension. As a result, the standard sample covariance estimator may yield a too large estimation error which in turn may yield significant system performance degradation with respect to the ideal channel covariance knowledge case. To address such problem, we propose a method based on a parametric representation of the channel angular scattering function. The proposed parametric representation includes a discrete specular component which is addressed using the well-known MUltiple SIgnal Classification (MUSIC) method, and a diffuse scattering component, which is modeled as the superposition of suitable dictionary functions. To obtain the representation parameters, we propose two methods, where the first solves a nonnegative least-squares problem and the second maximizes the likelihood function using expectation–maximization. Our simulation results show that the proposed methods outperform the state of the art with respect to various estimation quality metrics and different sample sizes.

Funder

Technische Universität Berlin

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Signal Processing

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Uplink to Downlink Channel Covariance Transformation in FDD Systems;IEEE Transactions on Signal Processing;2023

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