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Article Dans Une Revue International Journal of Network Management Année : 2004

Call Admission Control in Cellular Networks: A Reinforcement Learning Solution

Résumé

In this paper, we address the call admission control (CAC) problem in a cellular network that handles several classes of traffic with different resource requirements. The problem is formulated as a semi-Markov decision process (SMDP) problem. We use a real-time reinforcement learning (RL) [neuro-dynamic programming (NDP)] algorithm to construct a dynamic call admission control policy. We show that the policies obtained using our TQ-CAC and NQ-CAC algorithms, which are two different implementations of the RL algorithm, provide a good solution and are able to earn significantly higher revenues than classical solutions such as guard channel. A large number of experiments illustrates the robustness of our policies and shows how they improve quality of service (QoS) and reduce call-blocking probabilities of handoff calls even with variable traffic conditions.

Dates et versions

hal-01151090 , version 1 (12-05-2015)

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Sidi-Mohammed Senouci, André-Luc Beylot, Guy Pujolle. Call Admission Control in Cellular Networks: A Reinforcement Learning Solution. International Journal of Network Management, 2004, 14 (2), pp.89-103. ⟨10.1002/nem.510⟩. ⟨hal-01151090⟩
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