Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 13947-13963· 1 citation· 47 references
Abstract
Multi-access Edge Computing (MEC) can effectively process Internet of Things (IoT) data by transferring computing intensive tasks to edge servers, and has become an effective mechanism to meet the growing demand for computing. The flexible Uncrewed Aerial Vehicle (UAV) and High-Altitude Platform (HAP) with powerful resources working together can significantly improve the efficiency of edge computing system. This paper investigates the resource allocation and trajectory optimization problems in HAP-UAV-MEC system with a Non-Orthogonal Multiple Access (NOMA) communication scenario. By utilizing Wireless Power Transfer (WPT) technology to provide energy support for UAV, we jointly optimize UAV trajectories, resource allocation, and offloading decisions to minimize the energy cost of IoT devices and the energy cost of UAV. This problem is described as a multi-stage Mixed Integer Nonlinear Programming (MINLP) problem. A Deep Deterministic Policy Gradient (DDPG)-Attention-based Resource Allocation and Trajectory Optimization (DART) algorithm combining Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques is proposed to address this issue. DART algorithm utilizes the Lyapunov technique to transform the multi-stage MINLP problem into a deterministic optimization problem, and decomposes the original problem into four parallel subproblems. Through DDPG-attention algorithm based on reinforcement learning and deep learning attention mechanisms, we solve the problems of trajectory optimization and offloading decision. Meanwhile, for remaining subproblems related to resource allocation, convex optimization is used to solve them. The experimental results verify that the DART algorithm can significantly reduce the total cost while ensuring system stability and performance.
A Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory is proposed.
W. Saber, Hanan Algamil, Fifi Farouk et al.· Future Internet· 0 citations
The flexible deployment of uncrewed aerial vehicles (UAVs) and the wide-area coverage of low Earth orbit (LEO) satellites make their integration in space–air–ground integrated networks (SAGINs) a promising solution for communication in resource-constrained remote areas. This paper proposes a SAGIN framework supporting mobile edge computing (MEC) with a three-layer architecture, which provides heterogeneous computing resources for ground Internet of Things (IoT) devices and enables users in remote and underdeveloped regions to access computational services. Our objective is to minimize the weighted sum of energy consumption and latency in the SAGIN subject to satellite coverage time constraints and partial task offloading requirements. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) challenge that jointly optimizes the UAV’s three-dimensional trajectory, IoT device association, transmit power, and task assignment. The coupled optimization variables form a hybrid action space with both discrete and continuous actions. To address this challenge, a parameterized double deep Q-network (P-DDQN) algorithm based on deep reinforcement learning (DRL) is proposed. The proposed method employs the DDQN algorithm to handle discrete actions and the deep deterministic policy gradient (DDPG) algorithm to generate continuous actions. Simulation results show that the proposed algorithm outperforms several baseline schemes in terms of system cost, providing an efficient solution for highly coupled hybrid decision optimization problems in SAGINs.
Haosheng Chen, Haixia Cui, Peng Cao et al.· IEEE Transactions on Wireles...· 0 citations
This paper proposes a joint optimization algorithm for trajectory control and task offloading ratios based on multi-agent deep reinforcement learning. By jointly optimizing the flight trajectories of unmanned aerial vehicles (UAVs), user scheduling strategies, and task offloading ratios, the decoupled coordination of resource allocation and trajectory planning is achieved, thereby minimizing system delay and weighted energy consumption. An enhanced multi-agent proximal policy optimization algorithm, named FMAHPPO, is designed. Compared with existing benchmark algorithms, the FMAHPPO algorithm significantly reduces the total system overhead and effectively improves the energy efficiency and task processing success rate of multi-UAV swarms. This research provides a valuable theoretical foundation and algorithmic support for the collaborative management of edge resources in future space-air-ground integrated networks (SAGIN).
A task-driven offloading algorithm based on Balanced Multi-Agent Deep Deterministic Policy Gradient (BMADDPG) that reduces average task processing latency by approximately 22.67% and decreases total system cost by at least 18.32% under high-load scenarios.
A Lyapunov-based joint optimization framework for UAV-enabled MEC systems achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation.
Lei Li, Xue Gao, Quansheng Guan· Electronics· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
