Interference Alignment-Aided Base Station Clustering using Coalition Formation

TL;DR

This paper introduces a long-term clustering method for interference networks that balances interference mitigation and CSI overhead, using coalitional game theory and statistical CSI for efficient base station clustering.

Contribution

It presents a novel long-term throughput model and a low-complexity distributed clustering algorithm based on coalitional game theory, requiring limited communication.

Findings

The proposed method converges rapidly within a few iterations.

Numerical simulations confirm the effectiveness of the clustering approach.

The approach reduces CSI overhead while maintaining interference mitigation.

Abstract

Base station clustering is necessary in large interference networks, where the channel state information (CSI) acquisition overhead otherwise would be overwhelming. In this paper, we propose a novel long-term throughput model for the clustered users which addresses the balance between interference mitigation capability and CSI acquisition overhead. The model only depends on statistical CSI, thus enabling long-term clustering. Based on notions from coalitional game theory, we propose a low-complexity distributed clustering method. The algorithm converges in a couple of iterations, and only requires limited communication between base stations. Numerical simulations show the viability of the proposed approach.