A graph attention diffusion-based solution generator is shown to produce near-optimal offloading and resource allocation decisions across synthetic low-altitude MEC instances, outperforming random, alternating, graph-RL, and graph diffusion baselines.
TJCCT: A Two-Timescale Approach for UA V-Assisted Mobile Edge Computing,
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Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion
A graph attention diffusion-based solution generator is shown to produce near-optimal offloading and resource allocation decisions across synthetic low-altitude MEC instances, outperforming random, alternating, graph-RL, and graph diffusion baselines.