A Successive Optimization Approach to Pilot Design for Multi-Cell Massive MIMO Systems

TL;DR

This paper presents a new pilot design method for multi-cell Massive MIMO systems that reduces interference and improves estimation accuracy by decomposing the problem into distributed convex sub-problems solved via successive optimization.

Contribution

It introduces a successive optimization approach that transforms non-convex pilot design problems into convex LMIs for efficient distributed solutions in Massive MIMO systems.

Findings

Faster convergence compared to benchmark schemes

Higher accuracy in channel estimation

Effective mitigation of pilot contamination

Abstract

In this letter, we introduce a novel pilot design approach that minimizes the total mean square errors of the minimum mean square error estimators of all base stations (BSs) subject to the transmit power constraints of individual users in the network, while tackling the pilot contamination in multi-cell Massive MIMO systems. First, we decompose the original non-convex problem into distributed optimization sub-problems at individual BSs, where each BS can optimize its own pilot signals given the knowledge of pilot signals from the remaining BSs. We then introduce a successive optimization approach to transform each optimization sub-problem into a linear matrix inequality (LMI) form, which is convex and can be solved by available optimization packages. Simulation results confirm the fast convergence of the proposed approach and prevails a benchmark scheme in terms of providing higher accuracy.