Academic Journal
Deep-Reinforcement-Learning-Based Joint 3-D Navigation and Phase-Shift Control for Mobile Internet of Vehicles Assisted by RIS-Equipped UAVs
| Τίτλος: | Deep-Reinforcement-Learning-Based Joint 3-D Navigation and Phase-Shift Control for Mobile Internet of Vehicles Assisted by RIS-Equipped UAVs |
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| Συγγραφείς: | Eskandari, M, Savkin, AV |
| Πηγή: | IEEE Internet of Things Journal. 10:18054-18066 |
| Στοιχεία εκδότη: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| Έτος έκδοσης: | 2023 |
| Θεματικοί όροι: | anzsrc-for: 1005 Communications Technologies, anzsrc-for: 0805 Distributed Computing, anzsrc-for: 4605 Data Management and Data Science, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 4613 Theory Of Computation, anzsrc-for: 4602 Artificial Intelligence, 4605 Data Management and Data Science, anzsrc-for: 40 Engineering, 4613 Theory Of Computation, 46 Information and Computing Sciences, 4602 Artificial Intelligence, anzsrc-for: 4006 Communications Engineering, 7 Affordable and Clean Energy, 4006 Communications Engineering, 40 Engineering |
| Περιγραφή: | Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs), notably, in the context of Internet-of-things (IoT). However, the application of UAVs, as active aerial base stations (BSs)/relays, is questionable in the fifth-generation (5G) WCNs with quasi-optic millimeter wave (mmWave) and beyond in 6G (visible light) WCNs. Because path loss is high in 5G/6G networks that attenuate, even, the line-of-sight (LoS) communicating signals propagated by UAVs. Besides, the limited energy/size/weight of UAVs makes it cost-deficient to design aerial multi-input/output BSs for active beamforming to strengthen the signals. Equipping UAVs with the reconfigurable intelligent surface (RIS), a passive component, can help to address the problems with UAV-assisted communication in 5G and optical 6G networks. We propose adopting the RIS-equipped UAV (RISeUAV) to provide aerial LoS service and facilitate communication for mobile Internet-of-vehicles (IoVs) in an obstructed dense urban area covered by 5G/6G. RISeUAV-aided wireless communication facilitates vehicle-to-vehicle/everything communication for IoVs for updating IoT information required for sensor fusion and autonomous driving. However, autonomous navigation of RISeUAV for this purpose is a multilateral problem and is computationally challenging for being optimally implemented in real-time. We intelligently automated RISeUAV navigation using deep reinforcement learning to address the optimality and time complexity issues. Simulation results show the effectiveness of the method. |
| Τύπος εγγράφου: | Article |
| Περιγραφή αρχείου: | application/pdf |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2023.3277598 |
| Rights: | IEEE Copyright CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....898ee3b910357b6db7f11b10597b731d |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 23722541 |
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| DOI: | 10.1109/jiot.2023.3277598 |