1.
Nafees, Muhammad; Baniasadi, Mohammadamin; Hopgood, James R.; Safari, Majid; Thompson, John S.
Integrated Sensing and Communication for UAV Trajectory Optimization in Mixed FSO-RF Networks in Dynamic Weather Conditions Proceedings Article
In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, 2025, ISSN: 1558-2612, (ISSN: 1558-2612).
Abstract | Links | BibTeX | Tags: 6G mobile communication, Autonomous aerial vehicles, Backhaul networks, free-space optical (FSO), Integrated sensing and communication, Meteorology, millimeter wave (mmWave), Millimeter wave communication, Optical attenuators, Optical feedback, Optical integrated sensing and communication (O-ISAC), Optical sensors, Sixth-generation (6G), Throughput, unmanned aerial vehicles (UAVs)
@inproceedings{nafees_integrated_2025,
title = {Integrated Sensing and Communication for UAV Trajectory Optimization in Mixed FSO-RF Networks in Dynamic Weather Conditions},
author = {Muhammad Nafees and Mohammadamin Baniasadi and James R. Hopgood and Majid Safari and John S. Thompson},
url = {https://ieeexplore.ieee.org/document/10978163},
doi = {10.1109/WCNC61545.2025.10978163},
issn = {1558-2612},
year = {2025},
date = {2025-03-01},
urldate = {2025-10-08},
booktitle = {2025 IEEE Wireless Communications and Networking Conference (WCNC)},
pages = {1–6},
abstract = {Integrated sensing and communication (ISAC) is expected to transform data transmission and real-time sensing, enhancing sixth-generation (6G) networks. Free-space optical (FSO) communication is a key 6G backhaul solution, complementing radio frequency (RF) technologies like millimeter wave (mmWave) for improved network reliability. However, adverse weather can significantly reduce FSO link reliability due to atmospheric attenuation. Such adverse weather conditions also increase the level of back-scattered light, potentially enabling the real-time sensing of the atmospheric channel gain at the transmitter side. Therefore, this paper proposes a novel optical ISAC (O-ISAC) framework, where the back-scattered light from the FSO communication signal is used as the sensing feedback signal. This O-ISAC framework is analyzed considering a single-cell network aided by an unmanned aerial vehicle (UAV) to support edge users. The UAV is connected to the gateway via a FSO backhaul link while estimating the FSO channel gain based on the back-scattered light and dynamically optimizing its trajectory. The aim of this adaptive O-ISAC system is to maximize the end-to-end network throughput of the edge users while considering FSO backhaul capacity and the UAV's directional antenna beamwidth and bandwidth allocation. Numerical results demonstrate that UAV can effectively optimize its trajectory by adjusting the antenna beamwidth and downlink bandwidth allocation at different weather conditions. The proposed framework is tested using hourly visibility data from Edinburgh, demonstrating that optical channel sensing is crucial for the system's overall performance.},
note = {ISSN: 1558-2612},
keywords = {6G mobile communication, Autonomous aerial vehicles, Backhaul networks, free-space optical (FSO), Integrated sensing and communication, Meteorology, millimeter wave (mmWave), Millimeter wave communication, Optical attenuators, Optical feedback, Optical integrated sensing and communication (O-ISAC), Optical sensors, Sixth-generation (6G), Throughput, unmanned aerial vehicles (UAVs)},
pubstate = {published},
tppubtype = {inproceedings}
}
Integrated sensing and communication (ISAC) is expected to transform data transmission and real-time sensing, enhancing sixth-generation (6G) networks. Free-space optical (FSO) communication is a key 6G backhaul solution, complementing radio frequency (RF) technologies like millimeter wave (mmWave) for improved network reliability. However, adverse weather can significantly reduce FSO link reliability due to atmospheric attenuation. Such adverse weather conditions also increase the level of back-scattered light, potentially enabling the real-time sensing of the atmospheric channel gain at the transmitter side. Therefore, this paper proposes a novel optical ISAC (O-ISAC) framework, where the back-scattered light from the FSO communication signal is used as the sensing feedback signal. This O-ISAC framework is analyzed considering a single-cell network aided by an unmanned aerial vehicle (UAV) to support edge users. The UAV is connected to the gateway via a FSO backhaul link while estimating the FSO channel gain based on the back-scattered light and dynamically optimizing its trajectory. The aim of this adaptive O-ISAC system is to maximize the end-to-end network throughput of the edge users while considering FSO backhaul capacity and the UAV's directional antenna beamwidth and bandwidth allocation. Numerical results demonstrate that UAV can effectively optimize its trajectory by adjusting the antenna beamwidth and downlink bandwidth allocation at different weather conditions. The proposed framework is tested using hourly visibility data from Edinburgh, demonstrating that optical channel sensing is crucial for the system's overall performance.
2.
Wang, Zhipeng; Ng, Soon Xin; El-Hajjar, Mohammed
A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments Journal Article
In: IEEE Open Journal of Vehicular Technology, vol. 6, pp. 647–661, 2025, ISSN: 2644-1330.
Abstract | Links | BibTeX | Tags: 3D path planning, 3D spatial information compression, Autonomous aerial vehicles, Classification algorithms, Convergence, deep reinforcement learning, Navigation, Path planning, Principal component analysis, Search problems, Solid modeling, Three-dimensional displays, Training, training efficiency, unmanned aerial vehicles
@article{wang_3d_2025,
title = {A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments},
author = {Zhipeng Wang and Soon Xin Ng and Mohammed El-Hajjar},
url = {https://ieeexplore.ieee.org/document/10878448},
doi = {10.1109/OJVT.2025.3540174},
issn = {2644-1330},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Open Journal of Vehicular Technology},
volume = {6},
pages = {647–661},
abstract = {Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a 100 textbackslashtimes 100 textbackslashtimes 50 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.},
keywords = {3D path planning, 3D spatial information compression, Autonomous aerial vehicles, Classification algorithms, Convergence, deep reinforcement learning, Navigation, Path planning, Principal component analysis, Search problems, Solid modeling, Three-dimensional displays, Training, training efficiency, unmanned aerial vehicles},
pubstate = {published},
tppubtype = {article}
}
Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a 100 textbackslashtimes 100 textbackslashtimes 50 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.
3.
Yan, Hua; Chen, Yunfei
Optimum Distance for In-Flight UAV-to-UAV Wireless Charging Journal Article
In: IEEE Access, vol. 13, pp. 143914–143924, 2025, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: Aperture antennas, Autonomous aerial vehicles, Batteries, Energy Efficiency, Energy loss, far-field, Inductive charging, near-field, Receiving antennas, RF signals, Simultaneous wireless information and power transfer, Transmitting antennas, UAV communications, Wireless communication, Wireless communications, wireless power transfer (WPT)
@article{yan_optimum_2025,
title = {Optimum Distance for In-Flight UAV-to-UAV Wireless Charging},
author = {Hua Yan and Yunfei Chen},
url = {https://ieeexplore.ieee.org/document/11123803/},
doi = {10.1109/ACCESS.2025.3598733},
issn = {2169-3536},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Access},
volume = {13},
pages = {143914–143924},
abstract = {Wireless charging is a promising technology for communications using battery-powered unmanned aerial vehicles (UAVs). In this paper, the optimal distance for UAV-to-UAV in-flight wireless charging and communications is studied. Considering the practical applications, two schemes are proposed. In the first scheme, the discharging UAV (D-UAV) and the charged UAV (C-UAV) are aligned during charging, which requires the D-UAV and the C-UAV to remain relatively stationary. In the second scheme, the D-UAV and the C-UAV move during charging. For both schemes, we aim to maximize the received energy at the C-UAV under the condition that the minimum achievable rate for communications is met. Numerical results show that the optimal distance exists in the Fresnel zone. They also show that the optimal distance increases with the charging frequency. This work provides useful guidance for UAV in-flight wireless charging and communications system designs.},
keywords = {Aperture antennas, Autonomous aerial vehicles, Batteries, Energy Efficiency, Energy loss, far-field, Inductive charging, near-field, Receiving antennas, RF signals, Simultaneous wireless information and power transfer, Transmitting antennas, UAV communications, Wireless communication, Wireless communications, wireless power transfer (WPT)},
pubstate = {published},
tppubtype = {article}
}
Wireless charging is a promising technology for communications using battery-powered unmanned aerial vehicles (UAVs). In this paper, the optimal distance for UAV-to-UAV in-flight wireless charging and communications is studied. Considering the practical applications, two schemes are proposed. In the first scheme, the discharging UAV (D-UAV) and the charged UAV (C-UAV) are aligned during charging, which requires the D-UAV and the C-UAV to remain relatively stationary. In the second scheme, the D-UAV and the C-UAV move during charging. For both schemes, we aim to maximize the received energy at the C-UAV under the condition that the minimum achievable rate for communications is met. Numerical results show that the optimal distance exists in the Fresnel zone. They also show that the optimal distance increases with the charging frequency. This work provides useful guidance for UAV in-flight wireless charging and communications system designs.