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Deep Reinforcement Learning for Distributed Computation Offloading in Massive-user Mobile Edge Networks

This work investigates the distributed task offloading problem in a mobile edge computing network with a massive number of users and one single base station (BS). Each user can decide, in a distributed manner, whether to compute its task locally or to offload to a common edge server equipped at the BS for execution. If edge computing, the user transmits its task data along with a unique but potentially non-orthogonal pilot sequence to the BS using a grant-free multiple-access scheme, and then the BS detects the offloading user and decodes the task data. The misseddetection case may occur, in which the missed-detection probability increases with the total number of offloading users. Thus, the offloading decision of each user depends highly on the decisions of all other users. More specifically, if the number of users that decide to offload increases, the risk that each user is miss-detected becomes higher, resulting in extra system cost compared with local computing only. To solve this issue, we apply Deep Q-Network (DQN) on each user and assume that the user can perceive the actions of other users in the last time slot as the observed state. Then, users make decisions on task computation only based on the local task parameters and few information broadcasted by the BS. The simulation results demonstrate that the algorithm can significantly minimize the system computation cost and show remarkable advantages when the number of user is large.