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

作　　者：Shabeer Ahmad Jinling Zhang Ali Nauman Adil Khan Khizar Abbas Babar Hayat 

机构地区：[1]School of Electronic Engineering,Beijing University of Posts and Telecommunication,Beijing 1000876,China [2]Department of Information and Communication Engineering,Yeungnam University,Gyeongsan 38541,Republic of Korea [3]School of Information Engineering,Xi’an Eurasia University,Xi’an 710065,China [4]Department of Computer Science,Hanyang University,Seoul 04763,Republic of Korea

出　　处：《Tsinghua Science and Technology》2025年第1期418-432,共15页清华大学学报自然科学版(英文版)

基　　金：supported by the National Natural Science Foundation of China(No.62271063);the National Key Laboratory of Science and Technology on Vacuum Electronics,and the Director Fund of Beijing Key Laboratory of Space-ground Interconnection and Convergence.

摘　　要：The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)networks.There is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing costs.Unmanned Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and 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 a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)mechanism.An efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput maximization.The proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G environment.Through extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

关 键 词：Deep Reinforcement Learning(DRL) Unmanned Aerial Vehicles(UAVs) resource allocation energy efficiency 5G and beyond network 

分 类 号：TN9[电子电信—信息与通信工程]