Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness

TL;DR

This paper proposes a distributed beamforming optimization method for multicell TDD systems that approaches Pareto optimality with limited intercell communication, improving fairness and sum-rate performance.

Contribution

It introduces a novel two-step centralized algorithm and an iterative distributed algorithm based on uplink-downlink duality for multicell beamforming optimization.

Findings

Distributed algorithm achieves near-centralized performance

Improves sum-rate and fairness tradeoff

Outperforms Nash Bargaining at high SNR

Abstract

This paper addresses coordinated downlink beamforming optimization in multicell time-division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.