The Impact of Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

TL;DR

This paper investigates scalable user-centric cell-free wireless networks, demonstrating that a simple subspace projection scheme effectively mitigates pilot contamination, achieving near-ideal channel estimation performance.

Contribution

It introduces a subspace-based pilot decontamination method that approaches the performance of ideal partial CSI in scalable user-centric networks.

Findings

Subspace projection nearly matches perfect partial CSI performance.

Pilot contamination can be effectively mitigated using dominant channel subspaces.

The proposed method simplifies pilot decontamination in large-scale networks.

Abstract

We consider a scalable user-centric wireless network with dynamic cluster formation as defined by Bj\"ornsson and Sanguinetti. Several options for scalable uplink (UL) processing are examined including: i) cluster size and SNR threshold criterion for cluster formation; ii) UL pilot dimension; iii) local detection and global (per cluster) combining. We use a simple model for the channel vector spatial correlation, which captures the fact that the propagation between UEs and RRHs is not isotropic. In particular, we define the ideal performance based on ideal but partial CSI, i.e., the CSI that can be estimated based on the users to antenna heads cluster connectivity. In practice, CSI is estimated from UL pilots, and therefore it is affected by noise and pilot contamination. We show that a very simple subspace projection scheme is able to basically attain the same performance of perfect but partial CSI. This points out that the essential information needed to pilot decontamination reduces effectively to the dominant channel subspaces.