Smart City Gnosys

Smart city article details

Title	Reinforcement Learning-Based Anti-Jamming Solution For Aerial Ris-Aided Dense Dynamic Multi-User Environments
ID_Doc	44892
Authors	Ul Abideen Tariq Z.; Baccour E.; Erbad A.; Hamdi M.
Year	2024
Published	20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024
DOI	http://dx.doi.org/10.1109/IWCMC61514.2024.10592583
Abstract	In the 5G Advanced and 6G era, wireless communication systems face security challenges, notably adversarial interference from unknown jammers in multi-user scenarios. Reconfigurable Intelligent Surfaces (RIS) present a cost-effective solution due to their low power consumption and easy deployment. Existing RIS techniques typically address simple jamming scenarios with a single static jammer, focusing on a single objective. This study introduces a multi-objective optimization approach deploying UAV-mounted RIS to counter jamming threats in wireless communications within a densely populated smart city environment. The proposed solution aims to safeguard essential services from potential disruptions caused by malicious jamming attacks during public events. We employ Proximal Policy Optimization (PPO), a lightweight Deep Reinforcement Learning (DRL) technique, to concurrently optimize the trajectory of UAV and RIS passive beamforming to address computational complexity. The objectives include maximizing the average sum rate and minimizing energy consumption. Our experiments highlight the efficacy of the PPO-based strategy, demonstrating significant improvements in average sum rates and energy efficiency amid numerous mobile devices and moving jammers. Importantly, our proposed system model outperforms a baseline from related works in maximizing the sum rate and minimizing overall energy consumption. © 2024 IEEE.
Author Keywords	Anti-Jamming; Jamming; PPO; Proximal Policy Optimization; Reconfigurable Intelligent Surfaces; Reinforcement Learning; RIS; Wireless Communications

Similar Articles

Id	Similarity	Authors	Title	Published
44886	0.925	Tariq Z.U.A.; Baccour E.; Erbad A.; Hamdi M.	Reinforcement Learning For Resilient Aerial-Irs Assisted Wireless Communications Networks In The Presence Of Multiple Jammers	IEEE Open Journal of the Communications Society, 5 (2024)
46706	0.913	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)
38480	0.868	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)
16152	0.867	Kim J.; Park S.; Jung S.; Cordeiro C.	Cooperative Multi-Uav Positioning For Aerial Internet Service Management: A Multi-Agent Deep Reinforcement Learning Approach	IEEE Transactions on Network and Service Management, 21, 4 (2024)
5700	0.867	Oubbati O.S.; Alotaibi J.; Alromithy F.; Atiquzzaman M.; Altimania M.R.	A Uav-Ugv Cooperative System: Patrolling And Energy Management For Urban Monitoring	IEEE Transactions on Vehicular Technology (2025)
59291	0.866	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)
23253	0.859	DIamanti M.; Tsampazi M.; Tsiropoulou E.E.; Papavassiliou S.	Energy Efficient Multi-User Communications Aided By Reconfigurable Intelligent Surfaces And Uavs	Proceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021 (2021)
9694	0.858	Dhuheir M.; Erbad A.; Al-Fuqaha A.; Hamdaoui B.; Guizani M.	Aoi-Aware Intelligent Platform For Energy And Rate Management In Multi-Uav Multi-Ris System	IEEE Transactions on Network and Service Management (2025)
32085	0.856	Eskandari M.; Savkin A.V.	Integrating Uavs And Riss In Future Wireless Networks: A Review And Tutorial On Iots And Vehicular Communications	Future Internet, 16, 12 (2024)
47814	0.853	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)