Robust Optimization of Low-Altitude Communication and Computation Resources in Uncertain Environments

GONG Yucheng; LI Bin; WANG Xinyi; FEI Zesong

doi:10.11999/JEIT260090

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GONG Yucheng, LI Bin, WANG Xinyi, FEI Zesong. Robust Optimization of Low-Altitude Communication and Computation Resources in Uncertain Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260090

Citation:

GONG Yucheng, LI Bin, WANG Xinyi, FEI Zesong. Robust Optimization of Low-Altitude Communication and Computation Resources in Uncertain Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260090

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Robust Optimization of Low-Altitude Communication and Computation Resources in Uncertain Environments

doi: 10.11999/JEIT260090 cstr: 32379.14.JEIT260090

1.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: The National Natural Science Foundation of China (62471039)

Accepted Date: 2026-04-13
Rev Recd Date: 2026-04-13

Available Online: 2026-04-30

Abstract

Abstract

Objective Low-altitude edge computing network is utilized to provide flexible computing services and enhance coverage for user equipment. However, the quality of service is often compromised by the significant uncertainty in task data sizes and the inevitable position jitter of UAVs caused by environmental disturbances. Existing robust solutions typically rely on deterministic uncertainty sets, which are often too conservative to accurately capture the stochastic nature of task demands. To address these challenges, a robust energy minimization framework is proposed for multi-UAV MEC networks. The primary objective is to minimize the weighted sum of system energy consumption. This is achieved by establishing a joint optimization model that coordinates UAV flight trajectories, task splitting decisions, and the allocation of computation and communication resources, explicitly accounting for the dual uncertainties of task magnitude and flight positioning. Methods To address the non-convexity and high coupling of the optimization variables, the problem is first modeled as a Markov Decision Process (MDP). A comprehensive state space is defined to characterize real-time system dynamics, while a continuous action space is designed for trajectory control and resource management. A Distributionally Robust Optimization Soft Actor-Critic (DRO-SAC) algorithm is developed to solve this MDP. Within this framework, an ambiguity set is constructed based on the L1-norm distance to characterize the distributional uncertainty of task demands. A maximum entropy reinforcement learning mechanism is then employed to learn the optimal policy against the worst-case distribution within the ambiguity set. Through this approach, UAV trajectories and power allocation are jointly optimized to ensure system robustness against dynamic environmental fluctuations. Results and Discussions The performance of the proposed DRO-SAC algorithm is evaluated through simulation experiments. It is observed that DRO-SAC achieves faster convergence and higher rewards compared to DDPG and PPO (Fig. 3). In terms of energy consumption, superior efficiency is consistently demonstrated by the proposed method under varying user densities (Fig. 4). Furthermore, the system's robustness against position errors is verified, with energy fluctuations maintained at a low level (Fig. 5). Finally, dynamic trajectory adjustments are visualized, confirming effective user coverage and energy minimization (Fig. 6). Conclusions A joint optimization framework based on DRO-SAC is proposed in this paper to address the uncertainties of task data size and UAV flight jitter in multi-UAV assisted MEC networks. By constructing an ambiguity set for task demand distribution and optimizing the worst-case expected objective, the limitations of traditional deterministic models in dynamic environments are effectively overcome. The weighted system energy consumption is minimized while satisfying latency and safety constraints. Finally, the superior convergence stability and energy efficiency of the proposed scheme are demonstrated through simulation results, even under conditions of limited resources and severe environmental fluctuations.

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