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논문 기본 정보

자료유형
학술저널
저자정보
Qinghua Liu (Zhejiang Yuying College of Vocational Technology) Qingping Li (Zhejiang Yuying College of Vocational Technology)
저널정보
한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제17권 제4호
발행연도
2021.8
수록면
721 - 736 (16page)
DOI
10.3745/JIPS.01.0079

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For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm ofcomputing and communication resources based on reinforcement learning is proposed. The energyconsumption of task execution is defined as the maximum energy consumption of each user's task execution inthe system. Considering the constraints of task unloading, power allocation, transmission rate and calculationresource allocation, the problem of joint task unloading and resource allocation is modeled as a problem ofmaximum task execution energy consumption minimization. As a mixed integer nonlinear programmingproblem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcementlearning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basisof reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the jointoptimization of data task unloading and power control strategy is carried out for each terminal device, and thelocal computing model and task unloading model are built. The simulation results show that the total taskcomputation cost of the proposed algorithm is 5%?10% less than that of the two comparison algorithms underthe same task input. At the same time, the total task computation cost of the proposed algorithm is more than5% less than that of the two new comparison algorithms.

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