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Open Access | Just Accepted

Deep-EERA: DRL-based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks

Shabeer Ahmad1Jinling Zhang1( )Ali Nauman2Adil Khan3Khizar Abbas4Babar Hayat1

1 School of Electronic Engineering, Beijing University of Posts and Telecommunication 1000876, Beijing, China

2 Department of Information and Communiction Engineering, Yeungnam University, 38541, Gyeongsan , South Korea

3 School of Information Engineering, Xi’an Eurasia University, 710065, Shaanxi, China

4 Department of Computer Science, Hanyang University , Seoul, 04763, South Korea

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Abstract

The advent of innovative and interactive applications like online gaming, digital twins, smart healthcare, smart cities, Internet of Things (IoT) services, and various industrial sectors has created a demand for high data rates, seamless connection, and ultra-low latency. Meeting these diverse application requirements is very challenging for 5G and Beyond 5G (B5G) networks. So, there is a pressing requirement for a cost-effective solution to enhance the spectral efficiency of B5G networks in densely populated areas, enabling higher data rates and uninterrupted connectivity while minimizing costs. Unmanned Aerial Vehicles (UAVs) serving as Aerial Base Stations (ABSs) in communication networks emerged as an innovative, cost-effective solution for bolstering network capacity. They prove particularly effective in handling emergencies, disasters, device failures, and sudden surges in high-data-rate demands. Nevertheless, integrating UAVs into the B5G networks presents new challenges, including resource scarcity, energy efficiency, resource allocation, optimal power transmission control, and maximizing overall throughput. This paper presents an energy-efficient wireless network system enhanced by UAVs acting as Aerial BSs. We have introduced a Deep Reinforcement Learning (DRL) based Deep Deterministic Policy Gradient (DDPG) mechanism for optimal resource allocation with the twin goals of energy efficiency and average throughput maximization. Through extensive simulations, we validate the performance of our DDPG approach, demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

Tsinghua Science and Technology
Cite this article:
Ahmad S, Zhang J, Nauman A, et al. Deep-EERA: DRL-based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks. Tsinghua Science and Technology, 2024, https://doi.org/10.26599/TST.2024.9010071

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Received: 05 December 2023
Revised: 19 February 2024
Accepted: 08 April 2024
Available online: 14 May 2024

© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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