Team MMSE Precoding with Applications to Cell-free Massive MIMO

TL;DR

This paper introduces a novel distributed precoding method called TMMSE for cell-free massive MIMO, optimizing performance with transmitter-specific channel information and outperforming existing heuristics.

Contribution

It generalizes classical MMSE precoding to distributed systems using team theory, deriving necessary conditions and explicitly solving for key scenarios in cell-free massive MIMO.

Findings

Optimal TMMSE precoding outperforms heuristic methods.

Explicit solutions provided for local and serial CSIT sharing cases.

Numerical results confirm improved ergodic achievable rates.

Abstract

This article studies a novel distributed precoding design, coined team minimum mean-square error (TMMSE) precoding, which rigorously generalizes classical centralized MMSE precoding to distributed operations based on transmitter-specific channel state information (CSIT). Building on the so-called theory of teams, we derive a set of necessary and sufficient conditions for optimal TMMSE precoding, in the form of an infinite dimensional linear system of equations. These optimality conditions are further specialized to cell-free massive MIMO networks, and explicitly solved for two important examples, i.e., the classical case of local CSIT and the case of unidirectional CSIT sharing along a serial fronthaul. The latter case is relevant, e.g., for the recently proposed radio stripe concept and the related advances on sequential processing exploiting serial connections. In both cases, our optimal design outperforms the heuristic methods that are known from the previous literature. Duality arguments and numerical simulations validate the effectiveness of the proposed team theoretical approach in terms of ergodic achievable rates under a sum-power constraint.