TY - GEN
T1 - New Two-Stage Deep Reinforcement Learning for Task Admission and Channel Allocation of Wireless-Powered Mobile Edge Computing
AU - Chen, Xiaojing
AU - Dai, Weiheng
AU - Ni, Wei
AU - Wang, Xin
AU - Zhang, Shunqing
AU - Xu, Shugong
AU - Sun, Yanzan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.
AB - This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.
KW - deep Q-network
KW - energy-efficient
KW - Mobile edge computing
KW - resource allocation
KW - wireless power transfer
UR - http://www.scopus.com/inward/record.url?scp=85137259160&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9839061
DO - 10.1109/ICC45855.2022.9839061
M3 - Conference Proceeding
AN - SCOPUS:85137259160
T3 - IEEE International Conference on Communications
SP - 1208
EP - 1213
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -