Optimal Beamforming in Interference Networks with Perfect Local Channel Information

TL;DR

This paper develops a framework for optimal beamforming in multi-transmitter, multi-receiver interference networks, simplifying the design of Pareto optimal resource allocations by focusing on rank-1 transmit covariance matrices and analyzing power gain-regions.

Contribution

It introduces a novel framework for determining efficient beamforming vectors using power gain-region analysis, reducing complexity in interference network optimization.

Findings

Beamforming vectors are parameterized by real-valued parameters between zero and one.

Efficient beamforming vectors lie on a specific boundary of the power gain-region.

Additional power allocation may be necessary in certain scenarios.

Abstract

We consider settings in which T multi-antenna transmitters and K single-antenna receivers concurrently utilize the available communication resources. Each transmitter sends useful information only to its intended receivers and can degrade the performance of unintended systems. Here, we assume the performance measures associated with each receiver are monotonic with the received power gains. In general, the systems' joint operation is desired to be Pareto optimal. However, designing Pareto optimal resource allocation schemes is known to be difficult. In order to reduce the complexity of achieving efficient operating points, we show that it is sufficient to consider rank-1 transmit covariance matrices and propose a framework for determining the efficient beamforming vectors. These beamforming vectors are thereby also parameterized by T(K-1) real-valued parameters each between zero and one. The framework is based on analyzing each transmitter's power gain-region which is composed of all jointly achievable power gains at the receivers. The efficient beamforming vectors are on a specific boundary section of the power gain-region, and in certain scenarios it is shown that it is necessary to perform additional power allocation on the beamforming vectors. Two examples which include broadcast and multicast data as well as a cognitive radio application scenario illustrate the results.