Smart City Gnosys

Smart city article details

Title	Cooperative Multi-Uav Positioning For Aerial Internet Service Management: A Multi-Agent Deep Reinforcement Learning Approach
ID_Doc	16152
Authors	Kim J.; Park S.; Jung S.; Cordeiro C.
Year	2024
Published	IEEE Transactions on Network and Service Management, 21, 4
DOI	http://dx.doi.org/10.1109/TNSM.2024.3392393
Abstract	This paper proposes a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration in mobile access applications where the UAVs work as mobile base stations. The primary objective of the proposed algorithm is to establish reliable mobile access networks for vehicle-to-everything (V2X) communications. This paper jointly considers energy-efficient UAV operation and reliable wireless communication services for realizing robust mobile access services. For the energy-efficient UAV operation, the reward function formulation of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for the reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of reward function. Furthermore, this paper considers 60 GHz millimeter-wave (mmWave) mobile access for utilizing the benefits of i) ultra-wide-bandwidth for multi-Gbps high-speed communications and ii) high-directional communications for spatial reuse that is obviously good for avoiding interference among densely deployed users. Lastly, the comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms. © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Author Keywords	Aerial cellular access; autonomous vehicle; millimeter-wave; multi-agent deep reinforcement learning; non-terrestrial network; smart city

Similar Articles

Id	Similarity	Authors	Title	Published
59291	0.891	Liu S.; Sun G.; Teng S.; Li J.; Zhang C.; Wang J.; Du H.; Liu Y.	Uav-Enabled Collaborative Secure Data Transmission Via Hybrid-Action Multi-Agent Deep Reinforcement Learning	Proceedings - IEEE Global Communications Conference, GLOBECOM (2024)
38480	0.877	Vishnoi V.; Budhiraja I.; Garg D.; Garg S.; Choi B.J.; Hossain M.S.	Multiagent Deep Reinforcement Learning Based Energy Efficient Resource Management Scheme For Ris Assisted D2D Users In 6G-Aided Smart Cities Environment	Alexandria Engineering Journal, 116 (2025)
1087	0.87	Parvaresh N.; Kantarci B.	A Continuous Actor-Critic Deep Q-Learning-Enabled Deployment Of Uav Base Stations: Toward 6G Small Cells In The Skies Of Smart Cities	IEEE Open Journal of the Communications Society, 4 (2023)
23217	0.87	Wang X.; Li J.; Wu J.; Guo L.; Ning Z.	Energy Efficiency Optimization Of Irs And Uav-Assisted Wireless Powered Edge Networks	IEEE Journal on Selected Topics in Signal Processing, 18, 7 (2024)
59311	0.869	Ullah Z.; Al-Turjman F.; Moatasim U.; Mostarda L.; Gagliardi R.	Uavs Joint Optimization Problems And Machine Learning To Improve The 5G And Beyond Communication	Computer Networks, 182 (2020)
46706	0.868	Tariq Z.U.A.; Baccour E.; Erbad A.; Hamdi M.; Guizani M.	Rl-Based Adaptive Uav Swarm Formation And Clustering For Secure 6G Wireless Communications In Dynamic Dense Environments	IEEE Access, 12 (2024)
44892	0.867	Ul Abideen Tariq Z.; Baccour E.; Erbad A.; Hamdi M.	Reinforcement Learning-Based Anti-Jamming Solution For Aerial Ris-Aided Dense Dynamic Multi-User Environments	20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024 (2024)
11445	0.864	Yun W.J.; Ha Y.J.; Jung S.; Kim J.	Autonomous Aerial Mobility Learning For Drone-Taxi Flight Control	International Conference on ICT Convergence, 2021-October (2021)
38095	0.862	Dhuheir M.; Erbad A.; Hamdaoui B.; Belhaouari S.B.; Guizani M.; Vu T.X.	Multi-Agent Meta Reinforcement Learning For Reliable And Low-Latency Distributed Inference In Resource-Constrained Uav Swarms	IEEE Access, 13 (2025)
47814	0.86	Fu F.; Jiao Q.; Yu F.R.; Zhang Z.; Du J.	Securing Uav-To-Vehicle Communications: A Curiosity-Driven Deep Q-Learning Network (C-Dqn) Approach	2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings (2021)