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Smart city article details
| Title | Rl-Based Adaptive Uav Swarm Formation And Clustering For Secure 6G Wireless Communications In Dynamic Dense Environments |
|---|---|
| ID_Doc | 46706 |
| Authors | Tariq Z.U.A.; Baccour E.; Erbad A.; Hamdi M.; Guizani M. |
| Year | 2024 |
| Published | IEEE Access, 12 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2024.3455250 |
| Abstract | The wireless communication landscape in beyond 5G and 6G systems, particularly in dense smart city environments, presents significant interference challenges. UAV-mounted Reconfigurable Intelligent Surfaces (RIS) offer a promising solution to counter interference from unknown jammers. However, the system's dynamic nature, especially real-time fluctuations in device and jammer distribution and UAV resources, complicates UAV and RIS management. Current approaches, which rely on a single UAV-mounted RIS or a fixed number of UAVs covering static device clusters, fail to adapt to these dynamic conditions. Smaller swarms may lead to inadequate coverage, while larger swarms can cause inefficiency and higher energy consumption. Additionally, these approaches often target a single objective, such as maximizing sum rates or minimizing energy use, without considering UAV battery constraints. Our work introduces an adaptive UAV swarm formation and dynamic device clustering technique designed for efficient anti-jamming in dynamic multi-user clusters threatened by unknown jammers during critical public events. This approach creates a flexible UAV-borne RIS swarm that dynamically adjusts the number of UAVs and the clustering to real-time changes of mobile devices and jammers, ensuring uninterrupted operations through UAV recharging and swapping while conserving total energy by deploying the minimum sufficient number of UAVs. Using Reinforcement Learning (RL), our solution optimizes the number of UAVs, device-to-UAV associations, UAV trajectories, RIS phase shifts, and base station power to effectively balance the sum rate and energy consumption. Simulations demonstrate the superior performance of our approach in coverage, jamming mitigation, energy conservation, connectivity, and scalability compared to existing methods and baselines. © 2013 IEEE. |
| Author Keywords | Anti-jamming; clustering; proximal policy optimization (PPO); reconfigurable intelligent surfaces (RIS); reinforcement learning; swarm UAVs; wireless communications |
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