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
2.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096
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

Funds: National Natural Science Foundation of China (62471223), 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: 2026-05-12

Available Online: 2026-05-29

Abstract

Abstract

Objective In low-altitude communication networks, hybrid RF/FSO UAV relaying can effectively alleviate RF spectrum congestion and enhance uplink data aggregation efficiency. However, in obstacle-rich urban environments, FSO backhaul links are highly susceptible to blockage and may experience intermittent outages, resulting in a severe mismatch between the RF uplink arrival rate and the FSO backhaul service rate. Meanwhile, UAV trajectory planning is constrained by obstacle-avoidance and flight dynamics. To address these coupled challenges, this paper investigates an energy-efficiency maximization problem by jointly optimizing multiuser NOMA-based RF access and the UAV’s three-dimensional obstacle-avoiding trajectory, while incorporating buffer-assisted RF/FSO rate decoupling. Methods A time-slotted UAV relaying model where multiple ground users upload data to the UAV via an RF link using NOMA is considered in the paper. The UAV decodes the superposed signals using successive interference cancellation (SIC) and determines the decoding order in each slot according to the received power ranking. The successfully received data are then forwarded to the base station (BS) through an FSO backhaul link. Urban blockage is modeled using 3D geometric obstacles, and a visibility test is employed to determine whether each relevant link is in LOS or non-line-of-sight (NLOS), thereby capturing the spatially correlated and time-varying characteristics of the RF access rate and the intermittent FSO backhaul capacity. To suppress the blockage-induced mismatch between uplink and backhaul rates, a finite-capacity buffer is deployed at the UAV. In each slot, the forwardable amount is jointly limited by the instantaneous FSO backhaul capability and the amount of data available in the buffer, while buffer-capacity constraints prevent overflow. System energy efficiency is defined as the ratio of the cumulative data successfully delivered to the BS over the mission horizon to the UAV propulsion energy consumption, where the propulsion power is modeled as a function of the UAV’s velocity and acceleration to reflect the impact of flight dynamics. Under 3D flight-region boundaries, prescribed start, end locations, discrete-time kinematic equations, maximum velocity and acceleration limits, and obstacle collision-avoidance constraints, a non-convex optimization problem is formulated with cross-slot multiuser transmit powers and the UAV 3D trajectory as decision variables. Furthermore, an alternating optimization framework is developed. With a fixed trajectory, the propulsion energy is fixed and maximizing energy efficiency becomes equivalent to increasing the end-to-end successfully forwarded data, yielding a power-optimization subproblem. Due to NOMA coupling and logarithmic rate expressions, this subproblem remains non-convex and is handled via successive convex approximation (SCA). With fixed transmit powers, particle swarm optimization (PSO) is used to search candidate 3D trajectories in a continuous space. To ensure feasibility under strict dynamics and safety constraints, a quadratic-programming (QP) projection is employed to enforce velocity and acceleration constraints, and collision checks are performed on trajectory waypoints and inter-slot line segments to guarantee obstacle-free flight. These two optimization procedures are alternately performed, resulting in a joint design that satisfies flight-dynamics feasibility and collision avoidance while significantly improving energy efficiency. Results and Discussion Simulations are conducted in an urban airspace containing multiple users, a BS, and dense 3D obstacles. Blockage causes frequent LOS/NLOS switching as the UAV moves. Figures 2 and 3 present comparisons of the 3D trajectory and its planar projection, respectively. Compared to the initial trajectory, the optimized trajectory exhibits clear detours and necessary altitude adjustments, and achieves collision-free flight while satisfying velocity and acceleration constraints, thus validating the feasibility and safety of the proposed trajectory planning approach. Figure 4 presents the energy-efficiency convergence behavior under different user transmit-power budgets. The proposed alternating optimization typically stabilizes within a small number of outer iterations. Meanwhile, the converged energy efficiency increases with higher power budgets, demonstrating the synergy between power control and trajectory adaptation. Furthermore, Figure 5 depicts the buffer evolution over time. It is observed that the buffer gradually accumulates when the backhaul is blocked or experiences strong fading, and is quickly drained once the UAV enters regions where LOS backhaul becomes available and FSO capacity improves. In order to further quantify the buffering gain, Figure 6 compares the system energy efficiency achieved by the proposed buffering mechanism and the no-buffer scheme. Compared to the no-buffer scheme, the proposed mechanism enables store-and-forward-based temporal smoothing during backhaul interruptions, thereby significantly improving system energy efficiency. Figure 7 illustrates the energy-efficiency convergence behavior under different buffer capacities. It is observed that as the buffer capacity increases, the converged energy-efficiency level is significantly improved. This is because a larger buffer enhances the UAV’s ability to temporarily store incoming data, thereby effectively alleviating data accumulation and transmission blockage when the access-link rate and backhaul-link rate are mismatched or when the backhaul link is constrained. Figure 8 compares the performance of four benchmark schemes, namly a non-optimized baseline, a power optimization scheme, a trajectory optimization scheme, and the proposed joint power-and-trajectory optimization scheme. It is found that the coordinated design of power allocation and obstacle-avoiding trajectory substantially improves end-to-end energy efficiency, and that trajectory optimization often plays a more dominant role under blockage-limited conditions. Conclusion The paper investigates a hybrid RF/FSO UAV relaying scheme with NOMA and an onboard buffering mechanism for low-altitude urban communication environments. Given the dense obstacles, frequent blockage, the fragility of FSO links, and stringent flight-dynamics constraints, an energy-efficiency maximization problem is formulated for the joint optimization of multiuser NOMA power allocation and the UAV trajectory. Accordingly, an SCA-based power-allocation method and an obstacle-avoiding trajectory design combining PSO with QP projection are developed. The obtained trajectory satisfies flight-dynamics feasibility and collision-avoidance requirements while significantly improving throughput per unit propulsion energy. Simulation results demonstrate that the planned trajectory can effectively avoid obstacles, and the onboard buffer provides an effective cushion between RF access and FSO backhaul to mitigate rate mismatch. In addition, the proposed method consistently outperforms benchmark schemes in terms of energy efficiency. Meanwhile, the trajectory optimization is shown to be generally more effective than power allocation in improving the overall system performance.
- UAV relaying,
- non-orthogonal multiple access,
- energy efficiency,
- power allocation,
- trajectory planning

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