Pilot-Aided Distributed Multi-Group Multicast Precoding Design for Cell-Free Massive MIMO

TL;DR

This paper introduces a fully distributed multi-group multicast precoding method for cell-free massive MIMO systems, reducing training overhead and eliminating backhaul signaling, with iterative training and local optimization.

Contribution

It proposes a novel distributed precoding design using group-specific uplink training, eliminating backhaul CSI exchange, and analyzes convergence of local optimization methods.

Findings

Distributed precoding outperforms conventional local CSI-based methods.

The proposed scheme reduces training overhead significantly.

Convergence of local updates is validated against centralized solutions.

Abstract

We propose fully distributed multi-group multicast precoding designs for cell-free massive multiple-input multiple-output (MIMO) systems with modest training overhead. We target the minimization of the sum of the maximum mean squared errors (MSEs) over the multicast groups, which is then approximated with a weighted sum MSE minimization to simplify the computation and signaling. To design the joint network-wide multi-group multicast precoders at the base stations (BSs) and the combiners at the user equipments (UEs) in a fully distributed fashion, we adopt an iterative bi-directional training scheme with UE-specific or group-specific precoded uplink pilots and group-specific precoded downlink pilots. To this end, we introduce a new group-specific uplink training resource that entirely eliminates the need for backhaul signaling for the channel state information (CSI) exchange. The precoders are optimized locally at each BS by means of either best-response or gradient-based updates, and the convergence of the two approaches is analyzed with respect to the centralized implementation with perfect CSI. Finally, numerical results show that the proposed distributed methods greatly outperform conventional cell-free massive MIMO precoding designs that rely solely on local CSI.