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Deep Reinforcement Learning-based Task Offloading Decision in the Time Varying Channel | IEEE Conference Publication | IEEE Xplore

Deep Reinforcement Learning-based Task Offloading Decision in the Time Varying Channel


Abstract:

This paper proposes a dynamic task offloading decision control scheme to minimize the total delay to execute computation task taking into account the time-varying channel...Show More

Abstract:

This paper proposes a dynamic task offloading decision control scheme to minimize the total delay to execute computation task taking into account the time-varying channel. Specifically, we consider the practical task offloading process, where executing computation task is carried out over multiple channel coherence times. In order to make an accurate decision on the task offloading process performed over multiple channel coherence times, we utilize the model-free reinforcement learning, since environment dynamics of the system, channel transition probabilities, is challenging to estimate. We formulate a problem of minimizing the total delay of executing computation task based on a Markov decision process (MDP). In order to solve the MDP problem, we develop a model-free reinforcement learning algorithm. Simulation results show that our proposed scheme outperforms the conventional scheme.
Date of Conference: 31 January 2021 - 03 February 2021
Date Added to IEEE Xplore: 10 March 2021
ISBN Information:
Conference Location: Jeju, Korea (South)

Funding Agency:


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