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Resource Allocation for Mobile Edge Computing System Considering User Mobility with Deep Reinforcement Learning

Kairi TOKUDA, Takehiro SATO, Eiji OKI

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Summary :

Mobile edge computing (MEC) is a key technology for providing services that require low latency by migrating cloud functions to the network edge. The potential low quality of the wireless channel should be noted when mobile users with limited computing resources offload tasks to an MEC server. To improve the transmission reliability, it is necessary to perform resource allocation in an MEC server, taking into account the current channel quality and the resource contention. There are several works that take a deep reinforcement learning (DRL) approach to address such resource allocation. However, these approaches consider a fixed number of users offloading their tasks, and do not assume a situation where the number of users varies due to user mobility. This paper proposes Deep reinforcement learning model for MEC Resource Allocation with Dummy (DMRA-D), an online learning model that addresses the resource allocation in an MEC server under the situation where the number of users varies. By adopting dummy state/action, DMRA-D keeps the state/action representation. Therefore, DMRA-D can continue to learn one model regardless of variation in the number of users during the operation. Numerical results show that DMRA-D improves the success rate of task submission while continuing learning under the situation where the number of users varies.

Publication
IEICE TRANSACTIONS on Communications Vol.E107-B No.1 pp.173-184
Publication Date
2024/01/01
Publicized
2023/10/06
Online ISSN
1745-1345
DOI
10.1587/transcom.2023EBP3043
Type of Manuscript
PAPER
Category
Network

Authors

Kairi TOKUDA
  Kyoto University
Takehiro SATO
  Kyoto University
Eiji OKI
  Kyoto University

Keyword