TY - GEN
T1 - Latency optimization for multi-user NOMA-MEC offloading using reinforcement learning
AU - Yang, Peitong
AU - Li, Lixin
AU - Liang, Wei
AU - Zhang, Huisheng
AU - Ding, Zhiguo
N1 - Funding Information:
This work was supported in part by the Aerospace Science and Technology Innovation Fund of the China Aerospace Science and Technology Corporation, in part by the Shanghai Aerospace Science and Technology Innovation Fund (SAST2018045, SAST2016034 and SAST2017049), in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University. (Corresponding author: Lixin Li.)
Funding Information:
This work was supported in part by the Aerospace Science and Technology Innovation Fund of the China Aerospace Science and Technology Corporation, in part by the Shanghai Aerospace Science and Technology Innovation Fund (SAST2018045, SAST2016034 and SAST2017049), in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Both non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as important techniques in future wireless networks, and the combination of them has received attention recently. It has been demonstrated that in a dual-user scenario, the use of the NOMA can effectively reduce the latency and energy consumption of MEC offloading. However, the scenario of multiple users needs to be considered further, which is more practical. In this paper, we consider a NOMA-MEC system with multiple users and single MEC server, and investigate the problem of minimizing offloading latency. Through using the Reinforcement learning (RL) algorithm Deep Q-network (DQN) to select the users who offload at the same time without knowing the actions of other users in advance, we will obtain the optimal user combination state and minimize system offloading latency. Simulation results show that the proposed method can significantly reduce the system offloading latency in the multi-user scenario of applying NOMA to MEC.
AB - Both non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as important techniques in future wireless networks, and the combination of them has received attention recently. It has been demonstrated that in a dual-user scenario, the use of the NOMA can effectively reduce the latency and energy consumption of MEC offloading. However, the scenario of multiple users needs to be considered further, which is more practical. In this paper, we consider a NOMA-MEC system with multiple users and single MEC server, and investigate the problem of minimizing offloading latency. Through using the Reinforcement learning (RL) algorithm Deep Q-network (DQN) to select the users who offload at the same time without knowing the actions of other users in advance, we will obtain the optimal user combination state and minimize system offloading latency. Simulation results show that the proposed method can significantly reduce the system offloading latency in the multi-user scenario of applying NOMA to MEC.
KW - Deep Q-network
KW - Mobile Edge Computing
KW - Non-orthogonal Multiple Access
KW - Offloading Latency
UR - http://www.scopus.com/inward/record.url?scp=85070404684&partnerID=8YFLogxK
U2 - 10.1109/WOCC.2019.8770605
DO - 10.1109/WOCC.2019.8770605
M3 - Conference contribution
AN - SCOPUS:85070404684
T3 - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
BT - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th Wireless and Optical Communications Conference, WOCC 2019
Y2 - 9 May 2019 through 10 May 2019
ER -
