Energy-Efficient Trajectory Planning and Resource Optimization for UAV Relay Communications over Hybrid RF/FSO Links

LI Baolong; PAN Wenwei; JIANG Hao; FENG Simeng; WU Qihui

doi:10.11999/JEIT260139

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LI Baolong, PAN Wenwei, JIANG Hao, FENG Simeng, WU Qihui. Energy-Efficient Trajectory Planning and Resource Optimization for UAV Relay Communications over Hybrid RF/FSO Links[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260139

Citation:

LI Baolong, PAN Wenwei, JIANG Hao, FENG Simeng, WU Qihui. Energy-Efficient Trajectory Planning and Resource Optimization for UAV Relay Communications over Hybrid RF/FSO Links[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260139

Citation:

LI Baolong, PAN Wenwei, JIANG Hao, FENG Simeng, WU Qihui. Energy-Efficient Trajectory Planning and Resource Optimization for UAV Relay Communications over Hybrid RF/FSO Links[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260139

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Energy-Efficient Trajectory Planning and Resource Optimization for UAV Relay Communications over Hybrid RF/FSO Links

doi: 10.11999/JEIT260139 cstr: 32379.14.JEIT260139

1.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Chian
2.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, Chian
3.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics Key Laboratory of Electromagnetic Spectrum Spatial Cognitive Dynamic System, Ministry of Industry and Information Technology, Nanjing 211106, Chian

Funds: The National Natural Science Foundation of China (62471223), The Youth Science and Technology Talent Promotion Project of Jiangsu Province (JSTJ-2024-392)

Received Date: 2026-02-02
Accepted Date: 2026-05-12
Rev Recd Date: 2026-05-08

Available Online: 2026-05-29

Abstract

Abstract

Objective In low-altitude communication networks, hybrid Radio Frequency/Free-Space Optical (RF/FSO) Unmanned Aerial Vehicle (UAV) relaying can ease RF spectrum congestion and improve uplink data aggregation. However, in obstacle-rich urban environments, FSO backhaul links are vulnerable to blockage and intermittent outages. This creates a severe mismatch between the RF access-link rate and the FSO backhaul-link rate. UAV trajectory planning is also constrained by obstacle avoidance and flight dynamics. To address these coupled issues, this paper investigates an energy-efficiency maximization problem. Multiuser Non-Orthogonal Multiple Access (NOMA)-based RF access and the Three-Dimensional (3D) obstacle-avoiding UAV trajectory are jointly optimized, and buffer-assisted RF/FSO rate decoupling is incorporated. Methods A time-slotted UAV relaying model is considered, in which multiple ground users upload data to the UAV through an RF link using NOMA. The UAV decodes superposed signals by Successive Interference Cancellation (SIC), and the decoding order in each slot is determined according to the received-power ranking. The successfully received data are then forwarded to a Base Station (BS) through an FSO backhaul link. Urban blockage is modeled using 3D geometric obstacles. A visibility test is used to determine whether each relevant link is in Line-Of-Sight (LOS) or Non-Line-Of-Sight (NLOS), which captures the spatially correlated and time-varying RF access-link rate and intermittent FSO backhaul capacity. To suppress blockage-induced rate mismatch between the RF access link and the FSO backhaul link, an onboard finite-capacity buffer is deployed at the UAV. In each slot, the forwardable data amount is jointly limited by the instantaneous FSO backhaul capacity and the data available in the buffer, and buffer-capacity constraints are imposed to prevent overflow. System energy efficiency is defined as the ratio of cumulative data successfully delivered to the BS over the mission horizon to UAV propulsion energy consumption. Propulsion power is modeled as a function of UAV velocity and acceleration to reflect the effect of flight dynamics. Under 3D flight-region boundaries, prescribed start and end locations, discrete-time kinematic equations, maximum velocity and acceleration limits, and obstacle collision-avoidance constraints, a non-convex optimization problem is formulated. The decision variables are cross-slot multiuser transmit powers and the 3D UAV trajectory. An alternating optimization framework is then developed. For a fixed trajectory, propulsion energy is fixed, so maximizing energy efficiency is equivalent to increasing end-to-end successfully forwarded data. This yields a power-optimization subproblem. Because of NOMA coupling and logarithmic rate expressions, this subproblem remains non-convex and is solved by Successive Convex Approximation (SCA). For fixed transmit powers, Particle Swarm Optimization (PSO) is used to search candidate 3D trajectories in continuous space. To ensure feasibility under strict dynamics and safety constraints, Quadratic Programming (QP) projection is used to enforce velocity and acceleration constraints. Collision checks are performed for trajectory waypoints and inter-slot line segments to ensure obstacle-free flight. These two optimization procedures are performed alternately. The resulting joint design satisfies flight-dynamics feasibility and collision-avoidance requirements and improves energy efficiency. Results and Discussion Simulations are conducted in an urban airspace with multiple users, a BS, and dense 3D obstacles. Blockage causes frequent LOS/NLOS switching as the UAV moves. Fig. 2 and 3 compare the 3D trajectory and its planar projection, respectively. Compared with the initial trajectory, the optimized trajectory shows clear detours and necessary altitude adjustments. It achieves collision-free flight while satisfying velocity and acceleration constraints, thereby verifying the feasibility and safety of the proposed trajectory planning method. Fig. 4 shows the convergence of energy efficiency under different user transmit-power budgets. The proposed alternating optimization generally stabilizes within a small number of outer iterations. The converged energy efficiency increases with the power budget, indicating synergy between power control and trajectory adaptation. Fig. 5 shows buffer evolution over time. The buffer gradually accumulates data when the backhaul is blocked or experiences strong fading. It is quickly drained when the UAV enters regions with LOS backhaul and improved FSO capacity. To quantify buffering gain, Fig. 6 compares system energy efficiency between the proposed buffering mechanism and the no-buffer scheme. The proposed mechanism enables store-and-forward temporal smoothing during backhaul interruptions and improves system energy efficiency. Fig. 7 shows energy-efficiency convergence under different buffer capacities. As buffer capacity increases, the converged energy-efficiency level improves. A larger buffer enhances the UAV’s ability to temporarily store incoming data and reduces data accumulation and transmission blockage when RF access-link and FSO backhaul-link rates are mismatched or the backhaul link is constrained. Figure 8 compares four benchmark schemes, namely a non-optimized baseline, a power-optimization scheme, a trajectory-optimization scheme, and the proposed joint power-and-trajectory optimization scheme. The coordinated design of power allocation and obstacle-avoiding trajectory improves end-to-end energy efficiency. Trajectory optimization also plays a more dominant role under blockage-limited conditions. Conclusion This paper investigates a hybrid RF/FSO UAV relaying scheme with NOMA and an onboard buffering mechanism for low-altitude urban communication. Given dense obstacles, frequent blockage, FSO-link susceptibility, and strict flight-dynamics constraints, an energy-efficiency maximization problem is formulated for the joint optimization of multiuser NOMA power allocation and UAV trajectory. An SCA-based power-allocation method and an obstacle-avoiding trajectory design that combines PSO with QP projection are developed. The obtained trajectory satisfies flight-dynamics feasibility and collision-avoidance requirements and improves throughput per unit propulsion energy. Simulation results show that the planned trajectory can avoid obstacles, and that the onboard buffer provides an effective cushion between RF access and FSO backhaul to mitigate rate mismatch. The proposed method consistently outperforms benchmark schemes in energy efficiency. Trajectory optimization is also shown to be generally more effective than power allocation in improving overall system performance.
- Unmanned Aerial Vehicle (UAV) relaying,
- Non-Orthogonal Multiple Access (MOMA),
- Energy efficiency optimization,
- Power allocation,
- Trajectory planning

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