In this paper we study the call admission control problem to optimize the network operators' revenue guaranteeing the quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a Semi-Markov Decision Process, and we use a model based Reinforcement Learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learns it on-line. We will show how our policy provides better solution than a classic greedy algorithm.

A Reinforcement Learning Admission Control Algorithm for NGN

SURACI, VINCENZO
2008-01-01

Abstract

In this paper we study the call admission control problem to optimize the network operators' revenue guaranteeing the quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a Semi-Markov Decision Process, and we use a model based Reinforcement Learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learns it on-line. We will show how our policy provides better solution than a classic greedy algorithm.
2008
9780769533339
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/397
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