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Smart city article details
| Title | Deep Point Reinforcement Learning Approach For Sustainable Communications By Uav And Moving Interaction Station |
|---|---|
| ID_Doc | 18022 |
| Authors | Chen L.; Liu K.; Yang P.; Xiong Z.; An P.; Quek T.Q.S.; Zhang Z. |
| Year | 2025 |
| Published | IEEE Transactions on Vehicular Technology |
| DOI | http://dx.doi.org/10.1109/TVT.2025.3572350 |
| Abstract | Unmanned aerial vehicles (UAVs) have emerged as a critical component in the smart city, which can significantly enhance integrated sensing and communication (ISAC) performance. This paper mainly investigates the UAV-to-Vehicle (U2V) communication scenarios, where vehicles are represented as rigid shapes in the radar point cloud (RPC). The moving interaction station (MIS) is proposed to provide the sensing-assisted and wireless charging service for the UAV. The radio knowledge map (RKM) is introduced to improve the communication and energy efficiency of the UAV-ISAC system. Then, a joint optimization problem is formulated to complete the data collection and upload task by adjusting the UAV trajectory and vehicle access. To address this problem, a deep point reinforcement learning (DPRL) algorithm is proposed, which contains an RPC network, an RKM network, and a decision-making module. Herein, the RPC and RKM networks are designed to merge and map the vehicle RPC and RKM into the action spaces. The decision-making module selects actions from the action spaces to optimize the UAV trajectory and vehicle access. Simulation results show that the proposed DPRL algorithm outperforms the benchmarks, achieving approximately a 10.87% increase in channel capacity and a 24.08% enhancement in residual energy. © 1967-2012 IEEE. |
| Author Keywords | ISAC; moving interaction station; radar point cloud; radio knowledge map; reinforcement learning; UAV communications |
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