1. QoE-oriented resource allocation for dense cloud NOMA smallcell networks.
- Author
-
Shao, Hongxiang, Sun, Youming, Du, Zhiyong, Cai, Jihao, and Duan, Zhentao
- Subjects
- *
MULTIPLE access protocols (Computer network protocols) , *RESOURCE allocation , *MACHINE learning , *QUALITY of service , *NASH equilibrium - Abstract
In this paper, we investigate the resource allocation for dense cloud non-orthogonal multiple access smallcell networks (NOMA SCN), aimed at maximizing the users' quality of service (QoE). First, we construct a directed hypergraph to model the complex inter-interference relationship for cloud NOMA SCN. Then, we formulate the QoE-oriented channel allocation and user pairing problem in NOMA SCN as a local cooperation game. The game is proved to be an exact potential game. Moreover, the optimal pure strategy Nash equilibrium (PNE) in the proposed game can maximize the network QoE level. To achieve the optimal PNE in the proposed game, we redesign a directed-hypergraph-based multi-agent learning algorithm, which allows multiple non-coupled agents in directed hypergraph to simultaneously update their actions. Finally, simulation results are presented to validate the proposed learning scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF