TY - GEN
T1 - Efficient resource allocation for NOMA-MEC system in ultra-dense network
T2 - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
AU - Cheng, Qianqian
AU - Li, Lixin
AU - Sun, Yan
AU - Wang, Dawei
AU - Liang, Wei
AU - Li, Xu
AU - Han, Zhu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Mobile edge computing (MEC) has become a promising technology to reduce the computational pressure and task delay of the users. Meanwhile, non-orthogonal multiple access (NOMA) can effectively improve the utilization of spectrum resources. Considering the advantages of MEC and NOMA, this paper investigates the resource allocation problem of the uplink NOMA-MEC system in an ultra-dense network (UDN), where each user will offload tasks to the MEC server according to the offloading policy. The optimization goal is to minimize energy consumption and task delay of users, which can improve the quality of service (QoS) for users. Firstly, a user cluster matching algorithm (UCMA) is proposed to improve the data transmission rate of users. Then, the UDN as a mean field game (MFG) framework, and a novel mean field-deep deterministic policy gradient (MF-DDPG) algorithm is proposed to obtain the solution of MFG because the DDPG method can reduce the complexity of the solution. The simulation results show that the MF-DDPG algorithm not only converges faster, but also effectively optimizes the energy consumption and task delay of the users.
AB - Mobile edge computing (MEC) has become a promising technology to reduce the computational pressure and task delay of the users. Meanwhile, non-orthogonal multiple access (NOMA) can effectively improve the utilization of spectrum resources. Considering the advantages of MEC and NOMA, this paper investigates the resource allocation problem of the uplink NOMA-MEC system in an ultra-dense network (UDN), where each user will offload tasks to the MEC server according to the offloading policy. The optimization goal is to minimize energy consumption and task delay of users, which can improve the quality of service (QoS) for users. Firstly, a user cluster matching algorithm (UCMA) is proposed to improve the data transmission rate of users. Then, the UDN as a mean field game (MFG) framework, and a novel mean field-deep deterministic policy gradient (MF-DDPG) algorithm is proposed to obtain the solution of MFG because the DDPG method can reduce the complexity of the solution. The simulation results show that the MF-DDPG algorithm not only converges faster, but also effectively optimizes the energy consumption and task delay of the users.
KW - Mean field game (MFG)
KW - Mobile edge computing (MEC)
KW - Non-orthogonal multiple access (NOMA)
KW - Reinforcement learning (RL)
UR - http://www.scopus.com/inward/record.url?scp=85090265912&partnerID=8YFLogxK
U2 - 10.1109/ICCWorkshops49005.2020.9145070
DO - 10.1109/ICCWorkshops49005.2020.9145070
M3 - 会议稿件
AN - SCOPUS:85090265912
T3 - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
BT - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 June 2020 through 11 June 2020
ER -
