Call Admission Control for Multimedia Cellular Networks Using Neuro-dynamic Programming

Authors:
Sidi-Mohammed Senouci;André-Luc Beylot;Guy Pujolle
Affiliations:
-;-;-
Venue:
NETWORKING '02 Proceedings of the Second International IFIP-TC6 Networking Conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; and Mobile and Wireless Communications
Year:
2002

Citing 4
Cited 2

Machine Learning

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On optimal call admission control in cellular networks

INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 1
Call admission control and routing in integrated services networks using neuro-dynamic programming

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Adaptive call admission control under quality of service constraints: a reinforcement learning solution

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Call admission control in cellular networks: a reinforcement learning solution

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A general call admission policy for next generation wireless networks

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Abstract

We consider, in this paper, the call admission control (CAC) problem in a multimedia cellular network that handles several classes of traffic with different resource requirements. The problem is formulated as a Semi-Markov Decision Process (SMDP) problem. It is too complex to allow for an exact solution for this problem, so, we use a real-time neuro-dynamic programming (NDP) [Reinforcement Learning (RL)] algorithm to construct a dynamic call admission control policy. A broad set of experiments shows the robustness of our policies compared to the classical solutions such as Guard Channel.