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Dynamic Normalization TD3-Based Task Offloading for UAV-Assisted Collaborative Computing
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AI
DOI：10.1109/TNSM.2026.3667404.png)
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Abstract
En 中文
To meet the computational requirements of computation-intensive and delay-sensitive applications, we construct an Unmanned Aerial Vehicle (UAV)-assisted three-layer collaborative computing framework that integrates local, edge, and cloud computing resources. However, in dynamic UAV-assisted environments, some existing approaches lack adaptability and struggle to effectively balance delay and energy consumption. To address these challenges, we formulate a joint optimization problem that minimizes the weighted sum of delay and energy consumption, where adaptive weight factors are dynamically adjusted according to system state variations. Due to the non-convex and high-dimensional nature of our proposed problem, traditional optimization methods are generally inadequate. Hence, the problem is modeled as a Markov Decision Process (MDP), and a normalization-based reward function is designed to eliminate the dimensional imbalance between delay and energy consumption. A Dynamic Normalization Twin Delayed Deep Deterministic Policy Gradient (DN-TD3) algorithm is then proposed, which incorporates mechanisms of adaptive exploration and critic-driven policy updates to enhance convergence stability and reduce sensitivity to hyperparameters. Simulation results demonstrate that the proposed DN-TD3 algorithm outperforms benchmark schemes in terms of system cost reduction, convergence speed, and overall stability.
Keywords:
Computation offloading
mobile edge computing
unmanned aerial vehicle
deep reinforcement learning
quality of service
Journal
IF:
5.4
Papers:
515
Citations:
9.2K