Computation and Trajectory Optimization in Multi-UAV Edge Networks under Position Uncertainty

LU Min; LI Bin; ZENG Qian; WANG Xinyi

doi:10.11999/JEIT261123

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LU Min, LI Bin, ZENG Qian, WANG Xinyi. Computation and Trajectory Optimization in Multi-UAV Edge Networks under Position Uncertainty[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261123

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LU Min, LI Bin, ZENG Qian, WANG Xinyi. Computation and Trajectory Optimization in Multi-UAV Edge Networks under Position Uncertainty[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261123

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Computation and Trajectory Optimization in Multi-UAV Edge Networks under Position Uncertainty

doi: 10.11999/JEIT261123 cstr: 32379.14.JEIT261123

LU Min^1
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LI Bin^{1
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ZENG Qian^{2, 3
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WANG Xinyi^4
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1.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Physics and Electronics, Nanning Normal University, Nanning 530100, China
3.
Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning 530100, China
4.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: Mobile Information Networks-National Science and Technology Major Project (No. 2025ZD1304600), Open Project of Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing (XXGN202508)

Accepted Date: 2026-11-05
Rev Recd Date: 2026-11-05

Available Online: 2026-11-14

Abstract

Abstract

Objective Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks provide flexible computing services for user equipment. However, existing methods are generally optimized based on ideal position information and complete task data processing, making it difficult to effectively control transmission and computation overhead under UAV trajectory perturbations and user position uncertainties. To address these issues, semantic extraction is introduced, and an energy optimization problem under position uncertainty is investigated for multi-UAV cooperative MEC networks. Methods The non-convex energy minimization problem considering location uncertainty is decomposed into two coupled subproblems, namely UAV trajectory control and computation offloading-resource optimization. For the first subproblem, SAC is adopted to learn a continuous UAV control policy under location uncertainty. For the second subproblem, given the UAV positions, the task-upload UAV for each user is determined according to the predefined distance-based access rule. The continuous variables are then optimized using convex-optimization methods, while the task-execution variables are updated user by user via coordinate descent, resulting in the offloading decision and resource optimization algorithm (Algorithm 1). Algorithm 1 is further integrated with SAC to form the overall solution procedure (Algorithm 2). Results and Discussions The performance of the proposed scheme is evaluated through simulations. The reward convergence results show that the proposed SAC-based method achieves higher rewards and better convergence stability than DDPG and PPO algorithms (Fig. 2). For system energy consumption, the proposed scheme consistently achieves lower weighted average energy consumption under different user numbers compared with PPO, DDPG, GA, and the non-semantic scheme, confirming the effectiveness of semantic extraction in reducing task processing overhead (Fig. 3). The robustness against UAV position perturbations and user location deviations is further validated, where the energy consumption increases only slightly with increasing uncertainty levels (Fig. 4). The trajectory visualization results indicate that the UAVs can adjust their flight paths according to user distributions and achieve coordinated coverage, thereby improving energy efficiency in multi-UAV networks (Fig. 5). Conclusions An energy optimization scheme is proposed for UAV edge computing scenarios under position uncertainty, where semantic extraction is incorporated to reduce communication and computation overhead. The original optimization problem is divided into two parts, namely UAV trajectory control and task offloading with resource allocation, and solved by combining SAC with alternating optimization. Simulation results show that the proposed scheme maintains relatively stable performance under UAV position perturbations and user location deviations, demonstrating its effectiveness and robustness.
- Unmanned Aerial Vehicle (UAV) Communications,
- Mobile Edge Computing (MEC),
- Trajectory Disturbance,
- Position Uncertainty

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References(17)

References

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