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Record W426264565

Resource management for cross layered star and mesh networks

2008· dissertation· en· W426264565 on OpenAlexaff
Ayda Basyouni

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2008
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceComputer networkNetwork packetCellular networkWireless networkScheduling (production processes)WirelessTelecommunicationsEngineering
DOInot available

Abstract

fetched live from OpenAlex

The use of wireless services is rapidly spreading around the world and many of the world population no longer know how to cope without their cell phones; the feel of always being connected offers a great sense of flexibility and security. So far, voice has been the primary wireless application. However, with the Internet continuing to influence our daily lives, the demand for wireless data is extensively increasing. Already, in the countries that have cellular-data services readily available, the number of cellular subscribers taking advantage of data services has reached significant proportions. In this thesis, we investigate resource management techniques for cross layered star and mesh wireless data networks. In particular, we investigate several aspects related to resource management techniques over the reverse packet data channel in cdma2000 1xEV star networks. We provide an upper bound for the reverse packet data channel throughput as a function of the number of mobile stations that are allowed to transmit instantaneously on each time slot. We also provide a lower bound for the average sector throughput based on the number of users per sector and propose several autonomous rate assignment, and scheduling techniques that provide a significant throughput improvement relative to other published techniques. We also develop analytical models for lowest-rate-first, highest-rate-first priority scheduling techniques, and two round-robin fair scheduling techniques over the reverse data channel in cdma2000 1xEV star networks. For these four scheduling techniques, the distribution of the mobile stations among the possible data rates is modelled as a Markov process. An analytical expression for the steady state system throughput is derived from the steady state distribution of the above Markov process. The above model is extended to evaluate the performance of the cross layered design between the hybrid ARQ, rate assignment, and time slot scheduling over the reverse packet data channel in cdma2000 1xEV. Expressions for the steady state system throughput and file transmission delay are derived from the steady state distribution of this model. Fountain codes are a class of erasure codes with the property that a potentially limitless sequence of encoding symbols can be recovered from any subset of size equal to or slightly larger than the number of source symbols. In this thesis we relate the parameters of the higher layer Fountain codes to those of the physical layer codes to present a cross layered coding technique. Based on the bit error rate of physical layer codes, the upper layer Fountain code's parameters are designed to obtain a prespecified performance. The performance of the proposed cross layered coding technique is found to be comparable to that of physical layer based Hybrid ARQ technique. As an application we studied the performance of WiMAX backhaul mesh networks where cashing is allowed at the service stations and proposed cross layered coding technique is used as its main coding scheme. In particular, the performance of the above network is modelled as a Markov process and analytical expressions for the steady state system performance are derived from its associated steady state distribution. All the above analytical models are validated through simulations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.255
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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