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.
|Title of host publication||2021 International Conference on Electronics, Information, and Communication, ICEIC 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2021 Jan 31|
|Event||2021 International Conference on Electronics, Information, and Communication, ICEIC 2021 - Jeju, Korea, Republic of|
Duration: 2021 Jan 31 → 2021 Feb 3
|Name||2021 International Conference on Electronics, Information, and Communication, ICEIC 2021|
|Conference||2021 International Conference on Electronics, Information, and Communication, ICEIC 2021|
|Country||Korea, Republic of|
|Period||21/1/31 → 21/2/3|
Bibliographical noteFunding Information:
ACKNOWLEDGEMENTS This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2018R1A2A1A05021029) and in part by Sam-sung Research in Samsung Electronics.
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Electrical and Electronic Engineering