Graph Attention Network for Optimal User Association in Wireless Networks

TL;DR

This paper introduces a graph attention network-based method to optimize user association in 5G cellular networks, aiming to enhance energy efficiency by intelligently activating energy-saving features like cell switch off.

Contribution

It proposes a novel graph-based optimization approach for user association that outperforms traditional methods in improving network energy savings.

Findings

Proposed method improves energy savings over legacy approaches

Graph attention networks effectively model cellular network topologies

Energy-efficient user association reduces operational costs

Abstract

With increased 5G deployments, network densification is higher than ever to support the exponentially high throughput requirements. However, this has meant a significant increase in energy consumption, leading to higher operational expenditure (OpEx) for network operators creating an acute need for improvements in network energy savings (NES). A key determinant of operational efficacy in cellular networks is the user association (UA) policy, as it affects critical aspects like spectral efficiency, load balancing etc. and therefore impacts the overall energy consumption of the network directly. Furthermore, with cellular network topologies lending themselves well to graphical abstractions, use of graphs in network optimization has gained significant prominence. In this work, we propose and analyze a graphical abstraction based optimization for UA in cellular networks to improve NES by determining when energy saving features like cell switch off can be activated. A comparison with legacy approaches establishes the superiority of the proposed approach.