Improving call admission policies in wireless networks
Wireless Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Adaptive Bandwidth Reservation and Admission Control in QoS-Sensitive Cellular Networks
IEEE Transactions on Parallel and Distributed Systems
Call admission control in cellular networks: a reinforcement learning solution
International Journal of Network Management
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Call admission control in wireless networks: a comprehensive survey
IEEE Communications Surveys & Tutorials
Constraint optimization in call admission control domain with a NeuroEvolution algorithm
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
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This paper proposes an approach for learning call admission control (CAC) policies in a cellular network that handles several classes of traffic with different resource requirements. The performance measures in cellular networks are long term revenue, utility, call blocking rate (CBR) and handoff failure rate (CDR). Reinforcement Learning (RL) can be used to provide the optimal solution, however such method fails when the state space and action space are huge. We apply a form of NeuroEvolution (NE) algorithm to inductively learn the CAC policies, which is called CN (Call Admission Control scheme using NE). Comparing with the Q-Learning based CAC scheme in the constant traffic load shows that CN can not only approximate the optimal solution very well but also optimize the CBR and CDR in a more flexibility way. Additionally the simulation results demonstrate that the proposed scheme is capable of keeping the handoff dropping rate below a pre-specified value while still maintaining an acceptable CBR in the presence of smoothly varying arrival rates of traffic, in which the state space is too large for practical deployment of the other learning scheme.