Multi-agent Q-learning based Admission Control Mechanism in Heterogeneous Wireless Networks for Multiple Services

• Jiamei Chen,
• Yubin Xu,
• Lin Ma,
• Yao Wang,

Vol. 7, No. 10, October 28, 2013
10.3837/tiis.2013.10.003, Download Paper (Free):

• admission control
• multi-agent Q-learning
• Reinforcement Learning
• heterogeneous wireless network
• resource management

Abstract

In order to ensure both of the whole system capacity and users QoS requirements in heterogeneous wireless networks, admission control mechanism should be well designed. In this paper, Multi-agent Q-learning based Admission Control Mechanism (MQACM) is proposed to handle new and handoff call access problems appropriately. MQACM obtains the optimal decision policy by using an improved form of single-agent Q-learning method, Multi-agent Q-learning (MQ) method. MQ method is creatively introduced to solve the admission control problem in heterogeneous wireless networks in this paper. In addition, different priorities are allocated to multiple services aiming to make MQACM perform even well in congested network scenarios. It can be observed from both analysis and simulation results that our proposed method not only outperforms existing schemes with enhanced call blocking probability and handoff dropping probability performance, but also has better network universality and stability than other schemes.

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