Circulant matching method for multiplexing ATM traffic applied to video sources

Spaey, Kathleen; Blondia, Chris

doi:10.1007/978-0-387-35355-5_18

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…and the extension of this method for periodic transition matrices are described in (Spaey, 1997). The steps are based on the analogous steps in the matching procedure described in (Hwang, 1995).…”

Section: A Methods To Approximate the Superposition Of D-b Mapsmentioning

confidence: 99%

CAC investigation for video and data

Aarstad

Blaabjerg

Cerdan

et al. 1998

Broadband Communications

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A key objective of ATM-based networks is to provide at the same time guaranteed QoS to real time and non-real time services. This calls for thoroughly engineered traffic control methods for those service categories as well as for the overall integration strategy. The main objective of this paper is to investigate CAC in an ATM testbed with real switches and as realistic traffic as possible. The used traffic is video (MPEG model based on traces) and data modelled as traditional on/off sources. The video traffic is given priority over the data traffic. To complement and verify the experiments, two simulation tools using as input the artificial MPEG models and the real traces have been developed. The CAC boundary for the nonpriority case has also been derived analytically. The experimental sources are modelled as discrete-time Markov sources. A matching method to avoid state space explosion for the superposition is applied on the source models.

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Section: A Methods To Approximate the Superposition Of D-b Mapsmentioning

confidence: 99%

CAC investigation for video and data

Aarstad

Blaabjerg

Cerdan

et al. 1998

Broadband Communications

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“…Today, there are propagation models (Moltchanov et al, 2005;Heereman et al, 2011;Phaiboon et al, 2008;Mantilla et al, 2010), Protocol data units (PDU) error models (Khayam and Radha, 2003;Kong and Shwedyk, 1995;Wang and Moayeri, 1995;Kolar et al, 2011) and arrival models (Lombardo et al, 1998;Spaey and Blondia, 1998;Moltchanov et al, 2003;Loiseau et al, 2010;Dhomeja et al, 2011) all of which capture different aspects of the performance characteristics of WLANs. The RSSI, SNR, or PDU, bit or frame error models, though useful for design of transceivers, cannot be directly used in performance evaluation studies because they must be properly extended to higher layers which provide convenient characterisation of the dynamic nature of a wireless channel at the layer of interest (Moltchanov, 2010).…”

Section: Review Of Past Workmentioning

confidence: 98%

Empirical investigation on the dependence of TCP downstream throughput on SNR in an IEEE802.11b WLAN system

Oghogho

Edeko

Emagbetere

2017

Journal of King Saud University - Engineering Sciences

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The dependence of TCP downstream throughput (TCP down T) on signal to noise ratio (SNR) in an IEEE802.11b WLAN system was investigated in various environments and varieties of QoS traffic. TCP down T was measured for various SNR observed. An Infrastructure based IEEE802.11b WLAN system having networked computers on which measurement software were installed, was set up consecutively in various environments (open corridor, small offices with block walls and plaster boards and free space). Empirical models describing TCP down T against SNR for different signal ranges (all ranges of signals, strong signals only, grey signals only and weak signals only) were statistically generated and validated. As the SNR values changed from high (strong signals) through low (grey signals) to very low (weak signals), our results show a strong dependence of TCP down T on the received SNR. Our models showed lower RMS errors when compared with other similar models. We observed RMS errors of 0.6734791 Mbps, 0.472209 Mbps, 0.9111563 Mbps and 0.5764460 Mbps for general (all SNR) model, strong signals model, grey signals model and Weak signals model respectively. Our models will provide researchers and WLAN systems users with a tool to estimate the TCP downstream throughput in a real network in various environments by monitoring the received SNR. ª 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…In the second case, we obtain a transient chain by setting M = 5, a = .0001 and d = 1.1, meaning the load is very high and equals 4.5454. Finally, example three is the D-BMAP developed in [7] to approximate a superposition of various D-BMAPs, each used to model a variable bit rate source, by a circulant D-BMAP. In the first two examples both the block size m and the number of nonzero blocks N equals M + 1, while in the third we have N = 25 blocks each of size m = 6.…”

Section: M/g/1 Type Markov Chainsmentioning

confidence: 99%