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Record W2142490978 · doi:10.1109/glocom.2008.ecp.1040

Optimizing Throughput of UWB Networks with AMC, DRP, and Dly-ACK

2008· article· en· W2142490978 on OpenAlexaff
Ruonan Zhang, Lin Cai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer sciencePhysical layerComputer networkThroughputQueueing theoryCross-layer optimizationTransmission (telecommunications)WirelessWireless networkLink adaptationChannel (broadcasting)FadingTelecommunications

Abstract

fetched live from OpenAlex

In wireless networks, the physical layer adaptive modulation and coding (AMC) scheme has been proposed to improve bandwidth efficiency over the time-varying channel. In this paper, we study the performance of ultra-wideband (UWB) based wireless personal area network where AMC is coupled with the distributed reservation protocol (DRP) and the delayed- acknowledgement (Dly-ACK) schemes at the link layer. Considering the channel variation caused by the people shadowing effect, we first propose an analytical model using an embedded Markov chain to investigate the queuing behavior at sender's buffer. Second, the throughput optimization problem is formulated and the optimal transmission mode and payload length are obtained. Simulation results are given to validate the analysis. By jointly considering channel characteristics, physical layer and link layer transmission schemes, the analytical results of the paper can provide useful guidelines for cross-layer optimization, which is essential to ensure quality of services in UWB networks.

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 categoriesnone
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 score0.261
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.176
Teacher spread0.168 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2008
Admission routes1
Has abstractyes

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