Cell Selection in Wireless Two-Tier Networks: A Context-Aware Matching Game

TL;DR

This paper introduces a context-aware matching game approach for cell selection in small cell networks, leveraging user device and environment information to improve user association and network performance.

Contribution

It proposes a novel distributed algorithm for cell association based on a matching game with externalities, utilizing context information for better user-to-base station assignment.

Findings

Significant performance improvements over max-SINR approach.

The algorithm converges to a stable matching.

Enhanced utility per user with context-aware information.

Abstract

The deployment of small cell networks is seen as a major feature of the next generation of wireless networks. In this paper, a novel approach for cell association in small cell networks is proposed. The proposed approach exploits new types of information extracted from the users' devices and environment to improve the way in which users are assigned to their serving base stations. Examples of such context information include the devices' screen size and the users' trajectory. The problem is formulated as a matching game with externalities and a new, distributed algorithm is proposed to solve this game. The proposed algorithm is shown to reach a stable matching whose properties are studied. Simulation results show that the proposed context-aware matching approach yields significant performance gains, in terms of the average utility per user, when compared with a classical max-SINR approach.