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

GONG Yucheng¹,
LI Bin^{1, 2
,
,},
WANG Xinyi³,
FEI Zesong³

1.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing, Huzhou 313000, China
3.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: The National Natural Science Foundation of China (62471039), The Open Research Program of Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing (No. UMPIC202403)

Received Date: 2026-01-26
Accepted Date: 2026-04-13
Rev Recd Date: 2026-04-12

Available Online: 2026-04-30

Abstract

Abstract

Objective Low-altitude edge computing networks provide flexible computing services and extended coverage for user equipment. However, quality of service is often degraded by uncertainty in task data size and by Unmanned Aerial Vehicle (UAV) position jitter caused by environmental disturbances. Existing robust methods commonly rely on deterministic uncertainty sets, which tend to be conservative and cannot accurately describe the stochastic distribution of task demands. To address these challenges, a robust energy minimization framework is proposed for multi-UAV-assisted Mobile Edge Computing (MEC) networks. The objective is to minimize the weighted sum of system energy consumption. This is achieved by developing a joint optimization model that coordinates UAV flight trajectories, task splitting decisions, and computation and communication resource allocation. The model explicitly accounts for the dual uncertainties of task data size and UAV trajectory. Methods To handle the nonconvexity and strong coupling among 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, and a continuous action space is designed for trajectory control and resource management. A Distributionally Robust Optimization Soft Actor-Critic (DRO-SAC) algorithm is then developed to solve the MDP. In this framework, an ambiguity set based on the L1-norm distance is constructed to characterize the distributional uncertainty of the task demand distribution. A maximum-entropy reinforcement learning mechanism is used to learn an optimal policy under the worst-case distribution within the ambiguity set. In this way, UAV trajectories, task splitting, and computation and communication resource allocation are jointly optimized to improve system robustness under dynamic environmental fluctuations. Results and Discussions The performance of the proposed DRO-SAC algorithm is evaluated through simulations. DRO-SAC achieves faster convergence and higher rewards than Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) algorithms (Fig. 3). For energy consumption, the proposed method consistently achieves higher efficiency under different user densities (Fig. 4). The robustness of the system against position errors is also verified, with energy fluctuations kept at a low level (Fig. 5). Dynamic trajectory adjustment further confirms that the proposed method can provide effective user coverage while reducing system energy consumption (Fig. 6). Conclusions A DRO-SAC-based joint optimization framework is proposed to address uncertainty in task data size and UAV position jitter in multi-UAV-assisted MEC networks. By constructing an ambiguity set for the task demand distribution and optimizing the worst-case expected objective, the proposed method mitigates the limitations of traditional deterministic models in dynamic environments. Weighted system energy consumption is minimized while latency and safety constraints are satisfied. Simulation results demonstrate that the proposed scheme achieves stable convergence and high energy efficiency, even when communication and computation resources are limited and environmental parameters fluctuate strongly.

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References(15)

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