Belief Propagation Methods for Intercell Interference Coordination

TL;DR

This paper introduces a belief propagation framework for efficiently approximating interference coordination and resource allocation in wireless networks, enabling distributed solutions with low communication overhead.

Contribution

It develops a general, low-complexity belief propagation-based method for various wireless resource optimization problems, including power control and sub-band allocation.

Findings

Algorithms converge in just a few iterations in femtocell scenarios.

Methods achieve near-optimal performance with low communication.

Applicable to multiple interference management tasks.

Abstract

We consider a broad class of interference coordination and resource allocation problems for wireless links where the goal is to maximize the sum of functions of individual link rates. Such problems arise in the context of, for example, fractional frequency reuse (FFR) for macro-cellular networks and dynamic interference management in femtocells. The resulting optimization problems are typically hard to solve optimally even using centralized algorithms but are an essential computational step in implementing rate-fair and queue stabilizing scheduling policies in wireless networks. We consider a belief propagation framework to solve such problems approximately. In particular, we construct approximations to the belief propagation iterations to obtain computationally simple and distributed algorithms with low communication overhead. Notably, our methods are very general and apply to, for example, the optimization of transmit powers, transmit beamforming vectors, and sub-band allocation to maximize the above objective. Numerical results for femtocell deployments demonstrate that such algorithms compute a very good operating point in typically just a couple of iterations.