Optimizing UAV Trajectories in Optical IRS-Aided Hybrid FSO/RF Aerial Access Networks Using DRL Technique
DOI:
https://doi.org/10.4108/eetinis.124.10134Keywords:
Unmanned aerial vehicles (UAVs), Intelligent reflecting surface (IRS), Free Space Optics (FSO), Deep Reinforcement Learning (DRL)Abstract
This paper investigates hybrid free-space optics (FSO)/radio frequency aerial access networks (AANs) using a high-altitude platform (HAP) and multiple UAVs to dynamically serve terrestrial users under varying environmental conditions, such as atmospheric turbulence and cloud-induced attenuation. The optical intelligent reflecting surfaces (OIRS), mounted on the HAP, enhance the FSO signal distribution to multiple UAVs by enabling precise beam manipulation, improving link reliability, and increasing network scalability. A deep reinforcement learning (DRL)-based approach is developed to optimize UAV placement and user association in real time, maximizing end-to-end throughput while adhering to backhaul capacity constraints. The study takes into account FSO channel impairments, including path loss, turbulence-induced fading, and pointing misalignment, modeled using log-normal distributions. Numerical results demonstrate that the dynamic deployment of multi-UAV configuration, trained under realistic cloudy conditions, significantly outperforms single-UAV and static deployment strategies, achieving higher data rates and stable user connectivity. This work highlights the potential of deploying OIRS-assisted AANs supporting multiple UAVs to realize robust and high-performance 6G networks.
Downloads
References
[1] Nguyen, T.V., Le, H.D. and Pham, A.T. (2023) Onthe design of ris–uav relay-assisted hybrid fso/rf satellite–aerial–ground integrated network. IEEE Transactions on Aerospace and Electronic Systems 59(2): 757–771. doi:10.1109/TAES.2022.3189334.
[2] Zhou, D., Sheng, M., Li, J. and Han, Z. (2023) Aerospace integrated networks innovation for empowering 6g: A survey and future challenges. IEEE Communications Surveys & Tutorials 25(2): 975–1019. doi:10.1109/COMST.2023.3245614.
[3] France, T.A.S., Sweden, E., France, E.F.Q., Techcom, S., SIX, T., Cinterion, T., GreenerWave et al. (2024) Vision on non-terrestrial networks in 6g system (or imt-2030). In ETSI Conference on Non-Terrestrial Networks, a Native Component of 6G: 1–5.
[4] (2024), 3rd generation partnership project (3gpp) technologies - non-terrestrial networks (ntn), [Online]. Available: https://www.3gpp.org/technologies/ntn-overview.
[5] Khoshafa, M.H., Bueno, F., Ngatched, T.M.N. and Di Renzo, M. (2025) Ris-empowered secured spaceair-ground integrated networks: Opportunities and challenges. IEEE Communications Magazine 63(6): 130–136. doi:10.1109/MCOM.001.2400398.
[6] Nguyen, T.V., Le, H.D., Dang, N.T. and Pham, A.T. (2021) On the design of rate adaptation for relay-assisted satellite hybrid FSO/RF systems. IEEE Photon. J. : 1–1doi:10.1109/JPHOT.2021.3130720.
[7] Dabiri, M.T. and Hasna, M. (2025) A novel mrr-uavbased relay with optical network coding: A comparative study with optical irs and conventional uav relaying. IEEE Journal on Selected Areas in Communications 43(5): 1607–1620. doi:10.1109/JSAC.2025.3543516.
[8] Fu, S., Wei, W., Yin, L. and Zhao, L. (2025) Joint optimization of 3-d placement and transmission power for a relay based covert communication system. IEEE Transactions on Communications : 1–1doi:10.1109/TCOMM.2025.3585509.
[9] Wu, D., Sun, X. and Ansari, N. (2020) An fsobased drone assisted mobile access network for emergency communications. IEEE Transactions on Network Science and Engineering 7(3): 1597–1606. doi:10.1109/TNSE.2019.2942266.
[10] Wu, D., Sun, X. and Ansari, N. (2020) An fso-based drone charging system for emergency communications. IEEE Transactions on Vehicular Technology 69(12): 16155–16162. doi:10.1109/TVT.2020.3043969.
[11] Zhang, S. and Ansari, N. (2021) Latency aware 3d placement and user association in drone-assisted heterogeneous networks with fso-based backhaul. IEEE Transactions on Vehicular Technology 70(11): 11991–12000. doi:10.1109/TVT.2021.3112483.
[12] Lee, J.H., Park, K.H., Ko, Y.C. and Alouini, M.S. (2022) Spectral-efficient network design for highaltitude platform station networks with mixed rf/fso system. IEEE Transactions on Wireless Communications 21(9): 7072–7087. doi:10.1109/TWC.2022.3154401.
[13] Parvaresh, N., Kulhandjian, M., Kulhandjian, H., D’Amours, C. and Kantarci, B. (2022) A tutorial on ai-powered 3d deployment of drone base stations: State of the art, applications and challenges. Vehicular Communications 36: 100474. doi:https://doi.org/10.1016/j.vehcom.2022.100474, URL https://www.sciencedirect.com/science/article/pii/S2214209622000213.
[14] Yu, L., Sun, X., Shao, S., Chen, Y. and Albelaihi, R. (2023) Backhaul-aware drone base station placement and resource management for fso-based drone-assisted mobile networks. IEEE Transactions on Network Science and Engineering 10(3): 1659–1668. doi:10.1109/TNSE.2022.3233004.
[15] Guan, Y., Zou, S., Peng, H., Ni, W., Sun, Y. and Gao, H. (2024) Cooperative uav trajectory design for disaster area emergency communications: A multiagent ppo method. IEEE Internet of Things Journal 11(5): 8848–8859. doi:10.1109/JIOT.2023.3320796.
[16] Liu, S., Dahrouj, H. and Alouini, M.S. (2024) Joint user association and beamforming in integrated satellite-haps-ground networks. IEEE Transactions on Vehicular Technology 73(4): 5162–5178. doi:10.1109/TVT.2023.3329168.
[17] Nguyen, T.V., Le, H.D., Mai, V., R., S. and Pham, A.T. (2024) Deep reinforcement learning for uav placement over mixed fso/rf-based nonterrestrial networks. In 2024 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS): 1–5. doi:10.1109/APWCS61586.2024.10679285.
[18] Pham, P.D., Nguyen, C.K.P., Le, H.D., Pham, H.T.T., Nguyen, T.V. and Dang, N.T. (2024) Optical intelligent reflecting surface-assisted multiple users over turbulence channels. In 2024 RIVF International Conference on Computing and Communication Technologies (RIVF): 45–50. doi:10.1109/RIVF64335.2024.11009028.
[19] Le, H.D. and Pham, A.T. (2021) Level crossing rate and average fade duration of satellite-to-uav fso channels. IEEE Photonics Journal 13(1): 1–14. doi:10.1109/JPHOT.2021.3057198.
[20] Nguyen, T.V., Le, H.D., Dang, N.T. and Pham, A.T. (2021) Average transmission rate and outage performance of relay-assisted satellite hybrid fso/rf systems. In 2021 International Conference on Advanced Technologies for Communications (ATC): 1–6. doi:10.1109/ATC52653.2021.9598287.
[21] Do, T.H., Nguyen, H.Q., Le, H.D., Pham, H.T.T., Nguyen, T.V. and Dang, N.T. (2024) Mac protocols for uav-aided relaying optical ground-to-hap networks. In 2024 International Conference on Advanced Technologies for Communications (ATC): 992–997. doi:10.1109/ATC63255.2024.10908181.
[22] Le, H.D., Nguyen, T.V. and Pham, A.T. (2021) Cloud attenuation statistical model for satellite-based fso communications. IEEE Antennas and Wireless Propagation Letters 20(5): 643–647. doi:10.1109/LAWP.2021.3058641.
[23] Nguyen, T., Le, H., Pham, H. and Dang, N. (2023) Availability of free-space laser communication link with the presence of clouds in tropical regions. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 10(3). doi:10.4108/eetinis.v10i3.3327.
[24] Yahia, O.B., Erdogan, E., Kurt, G.K., Altunbas, I. and Yanikomeroglu, H. (2022) Haps selection for hybrid rf/fso satellite networks. IEEE Transactions on Aerospace and Electronic Systems 58(4): 2855–2867. doi:10.1109/TAES.2022.3142116.
[25] Nguyen, T.V., Pham, T.V., Dang, N.T. and Pham, A.T. (2020) Uav-based fso systems using sc-qam signaling over fading channels with misalignment. In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall): 1–5. doi:10.1109/VTC2020-Fall49728.2020.9348866.
[26] Le, H.D. and Pham, A.T. (2022) On the design of fsobased satellite systems using incremental redundancy hybrid arq protocols with rate adaptation. IEEE Transactions on Vehicular Technology 71(1): 463–477. doi:10.1109/TVT.2021.3127193.
[27] Samy, R., Yang, H.C., Rakia, T. and Alouini, M.S. (2022) Ergodic capacity analysis of satellite communication systems with sag-fso/sh-fso/rf transmission. IEEE Photonics Journal 14(5): 1–9. doi:10.1109/JPHOT.2022.3201046.
[28] Wang, H., Zhang, Z., Zhu, B., Dang, J., Wu, L. and Zhang, Y. (2022) Approaches to array-type optical irss: Schemes and comparative analysis. Journal of Lightwave Technology 40(12): 3576–3591. doi:10.1109/JLT.2022.3152812.
[29] Camp, T., Boleng, J. and Davies, V. (2002) A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing 2. doi:10.1002/wcm.72.
[30] Chu, T.M.C., Zepernick, H.J. and Duong, T.Q. (2022) Noma-based full-duplex uav network with k-means clustering for disaster scenarios. In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall): 1–7. doi:10.1109/VTC2022-Fall57202.2022.10012723.
[31] Khayat, G., Mavromoustakis, C.X., Pitsillides, A., Batalla, J.M. and Markakis, E.K. (2023) Multiple redundant k-means clustered scheme based on weighted cluster head selection for damaged s-uav. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference: 6177–6182. doi:10.1109/GLOBECOM54140.2023.10437501.
[32] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A. et al. (2015) Human-level control through deep reinforcement learning. Nature 518(7540): 529–533. doi:10.1038/nature14236.
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Cuong NGUYEN, Thang NGUYEN, Hien PHAM, Anh DO, Ngoc Dang
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
