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IEEE/ICACT20220171 Slide.08        [Big Slide]       [YouTube]	Oral Presentation
	To reduce very large computational complexity, we use a supervised learning approach. The DNN performs offloading and partitioning selections based on the given input values. The conventional DNN structure could exponentially increase the combination of classes between partitioning and offloading policy. To solve this problem, we divided a single DNN into two DNNs; One DNN for optimal work partitioning and the other for determining the offloading method for each component. We selected and saved the option and input data with minimum cost to generate a dataset for training deep neural network models. We randomly generated task size, number of components, division resolution, number of subcarriers, available computing resources of MES, the distance between UE and MES, frequency of UE, transmitting power of UE to consider simulations in various situations.

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