Globally Optimal Base Station Clustering in Interference Alignment-Based Multicell Networks

TL;DR

This paper introduces a branch and bound algorithm for finding the globally optimal base station clustering in interference alignment-based multicell networks, serving as a benchmark for suboptimal schemes and accounting for CSI overhead.

Contribution

It presents a novel branch and bound method that efficiently finds the optimal clustering, improving benchmarking capabilities for interference alignment in large networks.

Findings

The algorithm converges to the optimal solution using bounds and pruning.

Average complexity is significantly lower than exhaustive search.

Model accounts for long-term CSI statistics and overhead.

Abstract

Coordinated precoding based on interference alignment is a promising technique for improving the throughputs in future wireless multicell networks. In small networks, all base stations can typically jointly coordinate their precoding. In large networks however, base station clustering is necessary due to the otherwise overwhelmingly high channel state information (CSI) acquisition overhead. In this work, we provide a branch and bound algorithm for finding the globally optimal base station clustering. The algorithm is mainly intended for benchmarking existing suboptimal clustering schemes. We propose a general model for the user throughputs, which only depends on the long-term CSI statistics. The model assumes intracluster interference alignment and is able to account for the CSI acquisition overhead. By enumerating a search tree using a best-first search and pruning sub-trees in which the optimal solution provably cannot be, the proposed method converges to the optimal solution. The pruning is done using specifically derived bounds, which exploit some assumed structure in the throughput model. It is empirically shown that the proposed method has an average complexity which is orders of magnitude lower than that of exhaustive search.