Problem
Research questions and friction points this paper is trying to address.
UAV MEC
Pareto front
run-time control
scheduling
energy-delay trade-off
The Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling
🤖 AI Summary
为解决无人机边缘计算调度中的能耗与延迟权衡问题，提出PrefDT方法，通过离线训练单一模型实现运行时任意点调度。
📝 Abstract
A UAV mobile edge computing (MEC) fleet trades energy against delay, and its schedules form a Pareto front; we call a scheduler operable when the fleet can be asked for any point on that front at run time. We propose PrefDT, to the best of our knowledge the first preference-conditioned Decision Transformer for the problem of joint trajectory, association and offloading scheduling. Its idea comes from language modeling: we hand the model the desired trade-off as an input, such that a single model only needs to be trained once offline to return any desired point on the curve in one rollout. The fleet's state is summarized by attention pooling with a per-user bypass, so the scheduler keeps working when user reports are lost. The energy target is a running budget decremented by what the fleet actually spends. As a result, when wind or load pushes consumption off the plan, the policy can track the difference and hold its budget. Because no corpus of preference-labeled flights exists, we design a distillation pipeline and build the corpus by ourselves. In simulation against 26 method variants, PrefDT produces the best trade-off curve of any learned method and holds its energy budget to within 0.6% when propulsion cost rises by half in mid-flight.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Preference-Conditioned Decision Transformer
Pareto Front
Attention Pooling
Run-Time Control
Energy Budget
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