Algorithms for Interference Minimization in Future Wireless Network Decomposition

TL;DR

This paper introduces a fast clustering algorithm that reduces interference in dense future wireless networks, outperforming existing methods with minimal computational complexity.

Contribution

It presents a novel similarity-based clustering method with optional stable matchings for interference minimization in high-density wireless networks.

Findings

Outperforms state-of-the-art algorithms in interference reduction

Runs efficiently with matrix multiplication dominating complexity

Suitable for high-density urban wireless deployments

Abstract

We propose a simple and fast method for providing a high quality solution for the sum-interference minimization problem. As future networks are deployed in high density urban areas, improved clustering methods are needed to provide low interference network connectivity. The proposed algorithm applies straightforward similarity based clustering and optionally stable matchings to outperform state of the art algorithms. The running times of our algorithms are dominated by one matrix multiplication.