Performance evaluation for the sleep mode in the IEEE 802.16ebased on a queueing model with close-down time and multiplevacations

Zhanqiang Huo; Wuyi Yue; Naishuo Tian; Shunfu Jin

doi:10.3934/jimo.2009.5.511

Article Contents

2009, Volume 5, Issue 3: 511-524. Doi: 10.3934/jimo.2009.5.511

This issue Previous Article A Markovian approach to per-flow throughput unfairness in IEEE 802.11 multihop wireless networks Next Article Delay distribution and loss probability of bandwidth requests under truncated binary exponential backoff mechanism in IEEE 802.16e over Gilbert-Elliot error channel

Performance evaluation for the sleep mode in the IEEE 802.16e based on a queueing model with close-down time and multiple vacations

1.
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003
2.
Department of Intelligence and Informatics, Konan University, Kobe 658-8501
3.
College of Science, Yanshan University, Qinhuangdao 066004
4.
College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004

Received: August 31, 2008

Revised: March 31, 2009

Published: June 2009

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Abstract

IEEE 802.16e is the latest broadband wireless access standard designed to support mobility. In mobile networks, how to control energy consumption is one of the most important issues for the battery-powered mobile stations. The standard proposes an energy saving mechanism that named "sleep mode" for conserving the power of the mobile stations. According to the operation mechanism of the sleep mode for downlink traffic in the type I power saving class, a discrete-time Geom/G/1 queueing model with close-down time and multiple vacations is built. By employing an embedded Markov chain method and Little's law, the average queue length, the average sojourn time and the average busy cycle of the queueing model are derived. Correspondingly, we get the performance measures of the energy saving rate and the average packet delay time for the sleep mode in the IEEE 802.16e. Finally, numerical results are given to demonstrate the dependency relationships between the system performance measures and the system parameters. Furthermore, a cost model is developed to determine the optimum length of the close-down time for minimizing the total system cost.

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Mathematics Subject Classification: Primary: 90B10, 90B20; Secondary: 90C11, 76B75.

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