Nonstationary models of learning automata routing in data communication networks
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: an introduction
Learning automata: an introduction
On optimal call admission control in cellular networks
Wireless Networks
Continuous Learning Automata Solutions to the Capacity Assignment Problem
IEEE Transactions on Computers
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
A note on the learning automata based algorithms for adaptive parameter selection in PSO
Applied Soft Computing
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Dropping probability of handoff calls and blocking probability of new calls are two important QoS measures for cellular networks. Call admission policies, such as fractional guard channel and uniform fractional guard channel policies are used to maintain the pre-specified level of QoS. In this paper, we propose a learning automata based call admission policy in which a learning automaton is used to accept/reject new calls. This call admission policy can be considered as adaptive uniform fractional guard channel policy. In order to study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that for some range of input traffics, the performance of the proposed approach is close to the performance of the uniform fractional guard channel policy. The proposed policy is fully adaptive and doesn't require any information about the input traffics.