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Record W1494180053 · doi:10.1109/wcnc.1999.797850

QoS provisioning for wireless ATM by variable-rate coding

2003· article· en· W1494180053 on OpenAlexaff
K. Akhavan, Shayan Farahvash, M. Kavehrad, Nader Mehravari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsComputer scienceComputer networkQuality of serviceChannel (broadcasting)FadingTransmitterCode rateConvolutional codeChannel state informationBit error rateWirelessBandwidth (computing)Link adaptationCoding (social sciences)Real-time computingDecoding methodsTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

This paper presents a solution to the problem of providing quality-of-service (QoS) guarantees in an ATM network with wireless links. The suggested resolution to this problem is through selectable-rate channel coding. In particular, rate-compatible punctured convolutional (RCPC) codes are employed due to their several attractive features that make them favorable for ATM-based applications. In this adaptive scheme, the RCPC code rate is intelligently varied in response to the channel's nonstationary behavior according to the channel state information (CSI). This yields an equivalent stationary channel with a fixed and predetermined bit-error rate (BER) in which QoS can be guaranteed. In order to demonstrate the performance of RCPC codes in providing and maintaining the QoS, a lognormal fading channel model is assumed. An architecture is proposed for the wireless link. This system assumes that accurate CSI is available at the transmitter as well as at the receiver. Bandwidth utilization of the proposed system under various channel conditions is then evaluated, plotted, and discussed.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.026
GPT teacher head0.287
Teacher spread0.260 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2003
Admission routes1
Has abstractyes

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