Quantitative analysis of location management and QoS in wireless networks |
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| Authors: | Yan Zhang Laurence T. Yang Jianhua Ma Jun Zheng |
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| Affiliation: | 1. Simula Research Laboratory, Norway;2. St. Francis Xavier University, Canada;3. Hosei University, Japan;4. New Mexico Institute of Mining and Technology, USA;1. Research Center for Science and Technology for Learning, National Central University, Taoyuan, Taiwan;2. Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan;1. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba?tycka 5, 44-100 Gliwice, Poland;2. National Museum in Krakow, Laboratory of Analysis and Nondestructive Investigation of Heritage Objects (LANBOZ), Pi?sudskiego 14, 31-109 Kraków, Poland;1. Educational Sciences, Ordu University, Ordu, Turkey;2. Educational Sciences, Recep Tayyip Erdogan University, Rize, Turkey;1. Amity Institute of Information Technology, Amity University, Lucknow Campus, Lucknow-226028, India;2. IT-Department, Amity University, Lucknow Campus, Lucknow-226028, India |
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| Abstract: | As a fundamental component in wireless networks, location management consists of two operations: location update and paging. These two supplementary operations enable the mobile user ubiquitous mobility. However, in case of failed location update, a significant consequence is the obsolete location identity in the network databases and thereafter the incapability in establishing the valid route for the potential call connection, which will seriously degrade the network quality-of-service (QoS). This issue is not theoretically studied in the literature. In this paper, we perform a quantitative analysis of the location management effect on QoS in the wireless networks. The metrics call blocking probability and the average number of blocked calls are introduced to reflect the QoS. For the sake of general applicability, the performance metrics are formulated with the relaxed tele-traffic parameters. Namely, the call inter-arrival time, cell residence time, location area residence time and location update inter-arrival time follow a general probability density function. The formulae are additionally specified in the static and several dynamic location management mechanisms. Numerical examples are presented to show the interaction between the performance metrics and location management schemes. The discussions on the sensitivity of tele-parameters are also given. |
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