Paper Info
Title
Efficient Resource Allocation For Noma-Mec System In Ultra-Dense Network: A Mean Field Game Approach
Abstract
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.
Year
DOI
Venue
2020
10.1109/ICCWorkshops49005.2020.9145070
2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS)
Keywords
DocType
ISSN
Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), reinforcement learning (RL), mean field game (MFG)
Conference
2164-7038
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Qianqian Cheng141.14
Lixin Li2255.51
Yan Sun301.01
Dawei Wang401.69
Wei Liang52210.29
Xu Li6134.57
Zhu Han711215760.71