Structured channel covariance estimation from limited samples for large antenna arrays

In 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.

Automated summary

In massive multiuser multiple antenna systems, accurate estimation of the users' channel covariance matrix is crucial but challenging when the number of samples is limited compared to the channel vector dimension. To address this, we propose a method based on a parametric representation of the channel angular scattering function. The proposed methods outperform the state of the art in terms of estimation quality metrics and sample sizes.