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

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...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.59273-59288
Main Authors: Anany, Mohamed G., Elmesalawy, Mahmoud M., Ibrahim, Ibrahim I., El-Haleem, Ahmed M. Abd
Format: Article
Language:English
Subjects:
Algorithms
Autonomous aerial vehicles
Base stations
Cluster analysis
Clustering
HetNet
Internet of Things
IoT
Long Term Evolution
Machine learning
Matching
multi-RAT
Multimedia
Optimization
Radio equipment
Reinforcement learning
Throughput
UAV
Unmanned aerial vehicles
Vector quantization
Wireless communication
Wireless communications
Wireless fidelity
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3392597