Terahertz communication networks and intelligent reflecting surfaces exhibit
significant potential in advancing wireless networks, particularly within the
domain of aerial-based multi-access edge computing systems. These technologies
enable efficient offloading of computational tasks from user electronic devices
to Unmanned Aerial Vehicles or local execution. For the generation of
high-quality task-offloading allocations, conventional numerical optimization
methods often struggle to solve challenging combinatorial optimization problems
within the limited channel coherence time, thereby failing to respond quickly
to dynamic changes in system conditions. To address this challenge, we propose
a deep learning-based optimization framework called Iterative Order-Preserving
policy Optimization (IOPO), which enables the generation of energy-efficient
task-offloading decisions within milliseconds. Unlike exhaustive search
methods, IOPO provides continuous updates to the offloading decisions without
resorting to exhaustive search, resulting in accelerated convergence and
reduced computational complexity, particularly when dealing with complex
problems characterized by extensive solution spaces. Experimental results
demonstrate that the proposed framework can generate energy-efficient
task-offloading decisions within a very short time period, outperforming other
benchmark methods.

本文提出了一个基于深度学习的优化框架（IOPO），可以在毫秒之内生成高效节能的任务卸载决策，并且在处理解决方案空间广泛的复杂问题时，相较于其他基准方法性能更优。