Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks
University of Electronic Science and Technology of China · Hubei University of Technology · +2 more institutions
Abstract
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the…
Citation impact
- FWCI
- 28.09
- Percentile
- 100%
- References
- 54
Authors
6- NZNan ZhaoCorresponding
University of Electronic Science and Technology of China, Hubei University of Technology
- YLYing‐Chang Liang
University of Electronic Science and Technology of China
- DNDusit Niyato
Nanyang Technological University
- YPYiyang Pei
Singapore Institute of Technology
- MWMinghu Wu
Hubei University of Technology
Topics & keywords
- Computer science
- Reinforcement learning
- Cellular network
- Resource allocation
- Distributed computing
- Overhead (engineering)
- Wireless network
- Quality of service