Sequence Q-Learning Algorithm for Optimal Mobility-Aware User Association

TL;DR

This paper introduces the Sequence Q-learning Algorithm (SQA), an efficient method for optimizing user association in mobility-aware mmWave networks, considering user trajectories and reducing handover frequency.

Contribution

The paper presents a novel Q-learning based algorithm that optimizes user association with polynomial complexity, accounting for mobility and limiting handovers in mmWave networks.

Findings

SQA significantly outperforms benchmark algorithms in simulations.

SQA effectively manages handover frequency and improves long-term network throughput.

The algorithm is computationally efficient with polynomial time complexity.

Abstract

We consider a wireless network scenario applicable to metropolitan areas with developed public transport networks and high commute demands, where the mobile user equipments (UEs) move along fixed and predetermined trajectories and request to associate with millimeter-wave (mmWave) base stations (BSs). An effective and efficient algorithm, called the Sequence Q-learning Algorithm (SQA), is proposed to maximize the long-run average transmission rate of the network, which is an NP-hard problem. Furthermore, the SQA tackles the complexity issue by only allowing possible re-associations (handover of a UE from one BS to another) at a discrete set of decision epochs and has polynomial time complexity. This feature of the SQA also restricts too frequent handovers, which are considered highly undesirable in mmWave networks. Moreover, we demonstrate by extensive numerical results that the SQA can significantly outperform the benchmark algorithms proposed in existing research by taking all UEs' future trajectories and possible decisions into account at every decision epoch.