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Record W2931875541 · doi:10.1049/iet-com.2018.5954

Cross‐layer design of T‐ARQ and adaptive modulation and coding in a spectrum sharing with cooperative relaying system

2019· article· en· W2931875541 on OpenAlexaff
Mohammad Torabi, Somayeh Aliasghari, Chahé Nerguizian

Bibliographic record

VenueIET Communications · 2019
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsLink adaptationComputer scienceAutomatic repeat requestHybrid automatic repeat requestCoding (social sciences)Computer networkTelecommunicationsDecoding methodsMathematicsFadingTelecommunications linkStatistics

Abstract

fetched live from OpenAlex

This study presents an analysis for a cross‐layer design of a combined adaptive modulation and coding (AMC) with a truncated automatic repeat request (T‐ARQ) scheme in an underlay spectrum sharing cognitive radio employing an amplify‐and‐forward cooperative relaying system. The considered AMC employs a coded M‐ary quadrature amplitude modulation scheme combined with a T‐ARQ to improve the performance of the secondary user (SU) system in the considered cognitive radio. To establish the performance analysis, the cumulative distribution function (CDF) of the end‐to‐end signal‐to‐noise‐ratio at the receiver side of the SU system is derived. Then, using the derived CDF, closed‐form expressions are derived for three important performance metrics: the average spectral efficiency, the average packet‐error‐rate, and the outage probability. To evaluate the performance of the considered system, numerical results obtained from the derived closed‐form formulas are presented and compared. In addition, to verify the validity of the analysis, Monte–Carlo simulation results are also provided.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.075
GPT teacher head0.294
Teacher spread0.220 · 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
Published2019
Admission routes1
Has abstractyes

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