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Terahertz communication networks and intelligent reflecting surfaces exhibitsignificant potential in advancing wireless networks, particularly within thedomain of aerial-based multi-access edge computing systems. These technologiesenable efficient offloading of computational tasks from user electronic devicesto Unmanned Aerial Vehicles or local execution. For the generation ofhigh-quality task-offloading allocations, conventional numerical optimizationmethods often struggle to solve challenging combinatorial optimization problemswithin the limited channel coherence time, thereby failing to respond quicklyto dynamic changes in system conditions. To address this challenge, we proposea deep learning-based optimization framework called Iterative Order-Preservingpolicy Optimization (IOPO), which enables the generation of energy-efficienttask-offloading decisions within milliseconds. Unlike exhaustive searchmethods, IOPO provides continuous updates to the offloading decisions withoutresorting to exhaustive search, resulting in accelerated convergence andreduced computational complexity, particularly when dealing with complexproblems characterized by extensive solution spaces. Experimental resultsdemonstrate that the proposed framework can generate energy-efficienttask-offloading decisions within a very short time period, outperforming otherbenchmark methods.

A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System