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Multi-Task Parallel Execution-Oriented Content Caching, Computation Offloading and Channel Allocation in UAV-Assisted MEC Network
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AI
DOI：10.1109/tmc.2026.3674329.png)
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Abstract
En 中文
Leveraging flexible deployment, extensive coverage, and reliable communication links, uncrewed aerial vehicle (UAV)-assisted mobile edge computing offers new opportunities to support mobile devices with heavy computational tasks. Considering the energy constraint of UAVs and delay requirement of tasks, many efforts should be devoted to pursuing lower service latency, which however is under explored in this innovational architecture. In this paper, with the purpose of minimizing the overall network service duration, departing from traditional serial task execution, we first design a multi-task parallel execution paradigm, and then investigate a joint optimization problem encompassing content caching, computation offloading, and channel allocation. To address this intractable problem involving large state and action spaces, we decompose it into two subproblems, i.e., an intra content caching and computation offloading optimization of each UAV, and an inter channel allocation of all UAVs. We then propose a reinforcement learning-based two-layer optimization scheme that integrates the efficient representation of DQN learning and the comprehensive exploration of regret minimization learning. Specifically, in the lower layer, a DQN-based algorithm is developed to solve the intra subproblem, and in the upper layer, a regret minimization-based algorithm is designed to tackle the inter subproblem. Through nested optimization between the two layers, optimal strategies for content caching, computation offloading and channel allocation can be achieved. Numerical results demonstrate that the proposed scheme significantly reduces service latency compared to various baseline methods.
Keywords:
MEC
UAV
multi-task parallel execution
resource optimization
reinforcement learning
regret minimization
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