Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks

Abstract

Recently, the huge growth in multimedia demand derived from smart devices and Internet of Things (IoT) development requires efficient heterogeneous networks with high data rates. To fulfill these requirements, the exploitation of Unmanned Aerial Vehicles (UAVs) enabled with multiple radio access for providing efficient connectivity should be considered, since it offers flexibility, ease of deployment, and on-demand service for wireless devices. In this paper, the idea of deploying Multiple Radio Access Technologies (Multi-RAT) base stations on a UAV is proposed, in order to utilize the unlicensed spectrum. The problem of optimizing the Multi-RAT UAV’s location along with the wireless devices’ association to maximize the total system throughput considering a heterogeneous LTE and WLAN ground network is investigated. To solve this problem, we propose a novel framework based on reinforcement learning and regret matching algorithms, such that the Q-learning algorithm is used to find the Multi-RAT UAV’s optimum location, while regret matching is used to solve the optimum users’ association. Moreover, a K-means clustering algorithm is adopted as an initialization phase to speed up the convergence of the proposed solution. Simulation results show the significance of the proposed idea of deploying Multi-RAT base stations on a UAV. Further, the performance analysis shows the effectiveness of the proposed framework by applying K-means as an initialization phase.

Keywords

• UAV
• multi-RAT
• IoT
• HetNet
• reinforcement learning