Robust PCA for Subspace Estimation in User-Centric Cell-Free Wireless Networks

TL;DR

This paper introduces a robust PCA-based method for subspace estimation in user-centric cell-free massive MIMO networks, improving channel estimation accuracy under pilot contamination and limited pilot dimensions.

Contribution

It proposes a novel robust PCA approach for channel subspace estimation using SRS pilot signals, enhancing interference mitigation and channel estimation in distributed MIMO systems.

Findings

The proposed method achieves near-ideal system performance.

Latin squares based SRS pilot hopping effectively averages out interference.

Robust PCA improves channel estimation accuracy under pilot contamination.

Abstract

We consider a scalable user-centric cell-free massive MIMO network with distributed remote radio units (RUs), enabling macrodiversity and joint processing. Due to the limited uplink (UL) pilot dimension, multiuser interference in the UL pilot transmission phase makes channel estimation a non-trivial problem. We make use of two types of UL pilot signals, sounding reference signal (SRS) and demodulation reference signal (DMRS) pilots, for the estimation of the channel subspace and its instantaneous realization, respectively. The SRS pilots are transmitted over multiple time slots and resource blocks according to a Latin squares based hopping scheme, which aims at averaging out the interference of different SRS co-pilot users. We propose a robust principle component analysis approach for channel subspace estimation from the SRS signal samples, employed at the RUs for each associated user. The estimated subspace is further used at the RUs for DMRS pilot decontamination and instantaneous channel estimation. We provide numerical simulations to compare the system performance using our subspace and channel estimation scheme with the cases of ideal partial subspace/channel knowledge and pilot matching channel estimation. The results show that a system with a properly designed SRS pilot hopping scheme can closely approximate the performance of a genie-aided system.