Resource Allocation for NOMA-MEC Systems in Ultra-Dense Networks: A Learning Aided Mean-Field Game Approach

Lixin Li, Qianqian Cheng, Xiao Tang, Tong Bai, Wei Chen, Zhiguo Ding, Zhu Han

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

Attracted by the advantages of multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA), this article studies the resource allocation problem of a NOMA-MEC system in an ultra-dense network (UDN), where each user may opt for offloading tasks to the MEC server when it is computationally intensive. Our optimization goal is to minimize the system computation cost, concerning the energy consumption and task delay of users. In order to tackle the non-convexity issue of the objective function, we decouple this problem into two sub-problems: user clustering as well as jointly power and computation resource allocation. Firstly, we propose a user clustering matching (UCM) algorithm exploiting the differences in channel gains of users. Then, relying on the mean-field game (MFG) framework, we solve the resource allocation problem for intensive user deployment, using the novel deep deterministic policy gradient (DDPG) method, which is termed by a mean-field-deep deterministic policy gradient (MF-DDPG) algorithm. Finally, a jointly iterative optimization algorithm (JIOA) of UCM and MF-DDPG is proposed to minimize the computation cost of users. The simulation results demonstrate that the proposed algorithm exhibits rapid convergence, and is capable of efficiently reducing both the energy consumption and task delay of users.

Original languageBritish English
Article number9247446
Pages (from-to)1487-1500
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume20
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • deep deterministic policy gradient
  • deep reinforcement learning
  • mean-field game
  • Multi-access edge computing
  • non-orthogonal multiple access

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