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Record W2613418868 · doi:10.1109/wcnc.2017.7925925

Transmission Time Analysis for Adaptive Modulation System over Block Fading Channels

2017· article· en· W2613418868 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFadingLink adaptationTransmission (telecommunications)Computer scienceFading distributionCumulative distribution functionNetwork packetChannel (broadcasting)Channel state informationModulation (music)Electronic engineeringAlgorithmWirelessMathematicsComputer networkRayleigh fadingTelecommunicationsProbability density functionStatisticsPhysicsEngineering

Abstract

fetched live from OpenAlex

In this paper, we investigate the statistics of packet transmission time of wireless transmission systems employing adaptive modulation. Unlike traditional transmission systems, where the transmission time of a fixed-size packet is typically regarded as a constant, the transmission time of adaptive modulation systems depends on the channel realization as the transmission rate varies with the fading channel conditions. In this paper, we derive the exact statistical distribution of packet transmission time for adaptive modulation systems over block fading channels. The exact expressions of the probability mass function (PMF) and cumulative distribution function (CDF) of packet transmission time are obtained for both slow and fast fading scenarios. We further present an approximate PMF for fast fading scenario to reduce the computation complexity. Selected numerical results are presented to illustrate the mathematical formulation.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.488

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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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