Min-max fair coordinated beamforming in cellular systems via large systems analysis

TL;DR

This paper analyzes min-max fair coordinated beamforming in cellular systems, revealing asymptotic optimal strategies that depend only on channel statistics, simplifying implementation and providing performance insights.

Contribution

It introduces a large systems analysis for coordinated beamforming, deriving asymptotic optimal strategies that require only statistical channel knowledge.

Findings

Optimal beamforming has a nested zero-forcing structure.

Asymptotic analysis reduces optimization complexity.

Strategies depend only on channel statistics in large systems.

Abstract

This paper considers base station (BS) cooperation in the form of coordinated beamforming, focusing on min-max fairness in the power usage subject to target SINR constraints. We show that the optimal beamforming strategies have an interesting nested zero-forcing structure. In the asymptotic regime where the number of antennas at each BS and the number of users in each cell both grow large with their ratio tending to a finite constant, the dimensionality of the optimization is greatly reduced, and only knowledge of statistics is required to solve it. The optimal solution is characterized in general, and an algorithm is proposed that converges to the optimal transmit parameters, for feasible SINR targets. For the two cell case, a simple single parameter characterization is obtained. These asymptotic results provide insights into the average performance, as well as simple but efficient beamforming strategies for the finite system case. In particular, the optimal beamforming strategy from the large systems analysis only requires the base stations to have local instantaneous channel state information; the remaining parameters of the beamformer can be calculated using channel statistics which can easily be shared amongst the base stations.