Minimizing interference-to-signal ratios in multi-cell networks

TL;DR

This paper introduces a new deterministic clustering method for multi-cell wireless networks that reduces interference-to-signal ratios more efficiently than existing spectral clustering approaches, improving network performance.

Contribution

A novel deterministic clustering algorithm for multi-cell networks that is computationally efficient and yields higher quality interference minimization solutions.

Findings

Our method outperforms spectral clustering in synthetic simulations.

The algorithm is less computationally demanding than existing approaches.

Results show improved interference-to-signal ratios in network simulations.

Abstract

In contemporary wireless communication networks, base-stations are organized into coordinated clusters (called cells) to jointly serve the users. However, such fixed systems are plagued by the so-called cell-edge problem: near the boundaries, the interference between neighboring clusters can result in very poor interference-to-signal-power ratios. To achieve a high quality service, it is an important objective to minimize the sum of these ratios over the cells. The most common approach to solve this minimization problem is arguably the spectral clustering method. In this paper, we propose a new clustering approach, which is deterministic and computationally much less demanding than current methods. Simulating on synthetic instances indicates that our methods typically provide higher quality solutions than earlier methods. An earlier version of this algorithm was reported in arXiv:2111.00885.