Conference Paper

A Call Admission Control Scheme Using NeuroEvolution Algorithm in Cellular Networks.

Conference: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007
Source: DBLP

ABSTRACT

This paper proposes an approach for learning call admission control (CAC) policies in a cellular net- work 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 ac- ceptable 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.

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Available from: John Bigham, Dec 14, 2013
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    • "One of our previous published research papers [6]provided a different approach performing the CAC scheme through a form of NeuroEvolution (NE) algorithm called NeuroEvolution of Augmenting Topologies (NEAT) [7]. The objective of the algorithm is to maximize network utility and satisfy predefined QoS constraints. "
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