Open Access
ARTICLE
Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning
Yifei Wei1, *, Yinxiang Qu1, Min Zhao1, Lianping Zhang2, F. Richard Yu3
1 Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and
Telecommunications, Beijing, 100876, China.
2 Alibaba Cloud Computing, Hangzhou, 311121, China.
3 Department of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6, Canada.
* Corresponding Author: Yifei Wei. Email: .
Computers, Materials & Continua 2020, 63(3), 1515-1532. https://doi.org/10.32604/cmc.2020.09130
Received 13 November 2019; Accepted 01 March 2020; Issue published 30 April 2020
Abstract
Device-to-Device (D2D) communication is a promising technology that can
reduce the burden on cellular networks while increasing network capacity. In this paper, we
focus on the channel resource allocation and power control to improve the system resource
utilization and network throughput. Firstly, we treat each D2D pair as an independent agent.
Each agent makes decisions based on the local channel states information observed by itself.
The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user
system. We assume that the D2D pair do not possess any information on the availability
and quality of the resource block to be selected, so the problem is modeled as a stochastic
non-cooperative game. Hence, each agent becomes a player and they make decisions
together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm
based on game theory is established. Secondly, in order to accelerate the convergence rate
of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats
each group as an agent, and then performs multi-agent Q-learning algorithm to determine
the power for each group of D2D users. The simulation results show that the Q-learning
algorithm based on multi-agent can improve the throughput of the system. In particular,
FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while
improving system throughput.
Keywords
Cite This Article
Y. Wei, Y. Qu, M. Zhao, L. Zhang and F. Richard Yu, "Resource allocation and power control policy for device-todevice communication using multi-agent reinforcement learning,"
Computers, Materials & Continua, vol. 63, no.3, pp. 1515–1532, 2020. https://doi.org/10.32604/cmc.2020.09130
Citations