A novel approximate technique is proposed for the estimation of call blocking probabilities in cellular mobile telephony networks where call blocking triggers customer retrials. The approximate analysis technique is based on Markovian models with state spaces whose cardinalities are proportional to the maximum number of calls that can be simultaneously in progress within cells. The accuracy of the approximate technique is assessed by comparison against results of detailed simulation experiments, results of a previously proposed Markovian analysis approach, and upper and lower bounds to the call blocking probability. Numerical results show that the proposed approximate technique is very accurate, in spite of the remarkably small state spaces of the Markovian models.
Efficient estimation of call blocking probabilities in cellular mobile telephony networks with customer retrials / AJMONE MARSAN, Marco Giuseppe; DE CAROLIS, G.; Leonardi, Emilio; LO CIGNO, R.; Meo, Michela. - In: IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS. - ISSN 0733-8716. - 19:2(2001), pp. 332-346. [10.1109/49.914511]
Efficient estimation of call blocking probabilities in cellular mobile telephony networks with customer retrials
AJMONE MARSAN, Marco Giuseppe;LEONARDI, Emilio;MEO, Michela
2001
Abstract
A novel approximate technique is proposed for the estimation of call blocking probabilities in cellular mobile telephony networks where call blocking triggers customer retrials. The approximate analysis technique is based on Markovian models with state spaces whose cardinalities are proportional to the maximum number of calls that can be simultaneously in progress within cells. The accuracy of the approximate technique is assessed by comparison against results of detailed simulation experiments, results of a previously proposed Markovian analysis approach, and upper and lower bounds to the call blocking probability. Numerical results show that the proposed approximate technique is very accurate, in spite of the remarkably small state spaces of the Markovian models.Pubblicazioni consigliate
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https://hdl.handle.net/11583/1401839
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