Effective Channel Hybrid Estimation in User-Centric Distributed Massive MIMO Networks

Abstract

User-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO), commonly called cell-free mMIMO, is an essential technology for ensuring more uniform coverage and higher spectral and energy efficiencies in next-generation communication systems. This paper investigates an alternative effective channel estimation method to address the issue of the lower channel hardening degree experienced by UC D-mMIMO systems. Specifically, this paper proposes a hybrid approach for effective channel estimation, allowing users to estimate their channels either through downlink pilot-based training, by using a blind algorithm, or by relying on statistical channel state information (CSI). The hybrid estimation method is compared with the case where each method is applied individually, as well as with the ideal case of perfect CSI. The analysis is conducted using various system parameter settings, such as the number of antennas and users, and it accounts for the presence of pilot contamination. Simulation results reveal that the proposed hybrid channel estimation algorithm is able to provide the best spectral efficiency performance in any network parameter configuration, while reducing the estimation normalized mean-square error compared to conventional statistical CSI.