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Chapter 1Introduction pTDMA technique, which could not consequently be accurately modelled with existingmodels for either TDMA or random access mechanisms.In the area of this dissertation few works with an analytical approach have beenattempted. An exception was the work carried out by Sriram 2 [113]. He proposed asimplified analytical model to estimate the mean packet access delay of the IEEE802.14 and DOCISIS MAC protocols.In order to make a reliable estimation of mean access delay, issues such as contentionaccess delay (D c , the time a station takes to transmit a reservation request successfully)and queuing delays (W q ) should be addressed.For the approximation of queuing delays, according to Gross and Harris [45], thereliable result is given by the method of Pollaczek-Khintchine (PK). The author of[113] used the PK formula for queuing delays and for contention access delays (D c ) 3 , inhis work he used a simplified estimation given by a high percentile value of the meanplus five times the standard deviation. The same method, presented also in Sriram [112],was used to calculate the end-to-end packet delay (D ete ) 4 as the mean access delay plusfive times the standard deviation.We have used a similar approach to estimateDeteof the DVB/DAVIC protocol, and itwas found that the maximum number of users supported is considerably overestimateddue to the high percentile values used. For this reason, it is uncertain whether themethod of [113] can produce reliable results.1.2.3.2 Analysis of the stability of contention resolution algorithmsIn the literature, we have found that studies carried out in the past focused mainly on thestability of contention resolution algorithms (including the exponential backoff,2 The related work presented in this section will be cited the first time by the surname of the authors.3D c= F⋅ ( 2 + 3 ⋅ ρ ) , where F and ρ are the upstream frame duration and channel utilisation.4Dete= Dc+ Wq+ Tx+ 5 ⋅ σ , where TDx is the transmission delay of a packet and σDthe standarddeviation of access delay.1-7
Chapter 1Introduction ppolynomial backoff and the splitting tree algorithm) rather than their performancecharacteristics (e.g. access delay, system throughput and utilisation).a) Exponential backoff algorithm: In studies involving the exponential backoffalgorithm, Aldous [3] has proved that the exponential backoff algorithm is alwaysunstable in the infinite model for any positive arrival rate (λ) 5 . Studies carried out byHåstad et al. [48] also demonstrated that the binary exponential backoff algorithm isunstable for any λ above 0.568 even for a system with a finite numbers of stations.However, a recent study carried out by Goldberg et al. [39] suggested that thestability of the exponential backoff algorithm can possibly be as long as λ < 1/e in theinfinite model and with a finite expected delay (the average waiting time of messagesin the system).b) Polynomial backoff algorithm: Raghavan and Upfal [83] and the authors of [48]proved that this algorithm is always stable for any λ < 1 and for a finite number ofstations. However for the infinite model, Kelly and Mac<strong>Ph</strong>ee [62] proved that thepolynomial backoff algorithm is always unstable.b) Splitting tree algorithm: Most of the work related to the splitting tree algorithmfocuses on stability and the time required to resolve collisions, rather than estimatingthe total mean access delay. As an example, Greenberg et al. [44] used a hybridalgorithm based on a base 2 estimation algorithm (access probability = 2 -i ) and thesplitting tree algorithm of Capetanakis [14] and Tsybakov [119] to find an estimationof the multiplicity of conflicts ‘n’ and prove that the time to resolve conflicts ofmultiplicity is stable, achieving a maximum throughput = 0.4015. Cidon and Sidi[17] extended the idea of Greenberg and obtained an improved algorithm thatreduces the time to resolve conflicts maintaining stability for all λ up to 0.487.Most of these approaches work under the assumption that the number of nodes → ∞ inorder to support the independence of the transmission probability assumption. However,even if the technique for an infinite number of nodes was to be used, it has been proved5 λ is defined as the arrival rate per unit of time. In this section the unit of time is one slot.1-8
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Chapter 4Simulation and analytical
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Chapter 5Upstream channel capacity
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Chapter 6Optimisation of CRA using
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Chapter 7PERFORMANCE OPTIMISATION F
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Chapter 7Performance optimisation f
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aChapter 7Performance optimisation
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Chapter 8Final conclusions p8.2 Gen
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Chapter 8Final conclusions pWe have
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Chapter 8Final conclusions pApplica
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Chapter 8Final conclusions pTCIS st
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Referencesp[17] I. Cidon, and M. Si
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Referencesp[50] N. F. Huang, C. A.
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Referencesp[85] V. Rangel, R. Edwar
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Referencesp[112] K. Sriram, “Meth
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APPEDIX A: GLOSSARYAALABRADSLATMBCB
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Appendix AGlossary pkbpsLANLLCMACMA
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Appendix AGlossary pUSBUWVDSLVoDVoI
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Appendix BProtocol stack and packet
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Appendix BProtocol stack and packet
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APPENDIX C: D VERIFICATION TEST FOR
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Appendix D Verification test p209 2
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Appendix EResults for a 6 Mbps upst
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Appendix FGuide to CD-ROM p IEEE802
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