Mean value and tail distribution of the message delay in statistical multiplexers with correlated train arrivals

Vuyst, Stijn De; Wittevrongel, Sabine; Bruneel, Herwig

doi:10.1016/s0166-5316(02)00033-0

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Larger values of K imply that ON slots and OFF slots occur more clustered together in time and the arrival process is more bursty. The correlation parameter K is therefore also referred to as the burstiness factor or the burst-length factor of the arrival process [12,21]. Further, unless otherwise stated, we assume that the service times have a so-called shifted geometric distribution, i.e.…”

Section: Numerical Examplesmentioning

confidence: 99%

Performance analysis of the IEEE 802.16e sleep mode for correlated downlink traffic

et al. 2008

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In this paper, we evaluate the performance of the IEEE 802.16e sleep mode mechanism in wireless access networks. This mechanism reduces the energy consumption of a mobile station (MS) by allowing it to turn off its radio interface (sleep mode) when there is no traffic present at its serving base station (BS). After a sleep period expires, the MS briefly checks the BS for data packets and switches off for the duration of another sleep period if none are available. Specifically for IEEE 802.16e, each additional sleep period doubles in length, up to a certain maximum. Clearly, the sleep mode mechanism can extend the battery life of the MS considerably, but also increases the delay at the BS buffer. For the performance analysis, we use a discrete-time queueing model with general service times and multiple server vacations. The vacations represent the sleep periods and have a length depending on the number of preceding vacations. Unlike previous studies, we take the (short-range) traffic correlation into account by assuming a D-BMAP arrival process, i.e. the distribution of the number of packet arrivals per slot is modulated by the transitions in a Markov chain with N background states. As results, we obtain the distribution of the number of packets in the queue at various sets of time epochs, the distribution of the packet delay and the antenna activity rate. We apply these results to the IEEE 802.16e sleep mode mechanism with correlated downlink traffic. By means of some examples, we show the influence of both the configuration parameters and the traffic correlation on the delay and the energy consumption.

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Section: Numerical Examplesmentioning

confidence: 99%

Performance analysis of the IEEE 802.16e sleep mode for correlated downlink traffic

et al. 2008

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“…Larger values of K imply that on-slots and off-slots occur more clustered together in time and the arrival process is more bursty. The correlation parameter K is therefore also referred to as the burstiness factor or the burst-length factor of the arrival process [24], [25]. As transmission times have a fixed length of 1 slot, the load ρ is equal to ρ = λσ.…”

Section: Practical Examplementioning

confidence: 99%

Performance of the Sleep-Mode Mechanism of the New IEEE 802.16m Proposal for Correlated Downlink Traffic

Turck

Vuyst

Fiems

et al. 2009

Lecture Notes in Computer Science

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Abstract. There is a considerable interest nowadays in making wireless telecommunication more energy-efficient. The sleep-mode mechanism in WiMAX (IEEE 802.16e) is one of such energy saving measures. Recently, Samsung proposed some modifications on the sleep-mode mechanism, scheduled to appear in the forthcoming IEEE 802.16m standard, aimed at minimizing the signaling overhead. In this work, we present a performance analysis of this proposal and clarify the differences with the standard mechanism included in IEEE 802.16e. We also propose some special algorithms aimed at reducing the computational complexity of the analysis.

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“…In some situations, random theory has been applied to deal with this uncertain queuing problem data. For example, the servicing and arrival rates have been usually considered stochastic distributions (De Vuyst et al., ; Ivnitski, ). Although it is well known that random modeling techniques are useful tools in dealing with queuing problem uncertainty, in the real world, there are many nonprobabilistic factors that affect large‐scale hydropower construction projects, and therefore, fuzzy theory is more suitable.…”

Section: Introductionmentioning

confidence: 99%

Antithetic Method‐Based Particle Swarm Optimization for a Queuing Network Problem with Fuzzy Data in Concrete Transportation Systems

Zeng

Shen

2014

Computer aided Civil Eng

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Mean value and tail distribution of the message delay in statistical multiplexers with correlated train arrivals

Cited by 12 publications

References 17 publications

Performance analysis of the IEEE 802.16e sleep mode for correlated downlink traffic

Performance analysis of the IEEE 802.16e sleep mode for correlated downlink traffic

Performance of the Sleep-Mode Mechanism of the New IEEE 802.16m Proposal for Correlated Downlink Traffic

Antithetic Method‐Based Particle Swarm Optimization for a Queuing Network Problem with Fuzzy Data in Concrete Transportation Systems

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