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Record W2021924106 · doi:10.1049/iet-com.2012.0596

Performance analysis of adaptive <i>M</i> ‐ary quadrature amplitude modulation for amplify‐and‐forward opportunistic relaying under outdated channel state information

2013· article· en· W2021924106 on OpenAlexafffund
Mohammad Torabi, Jean‐François Frigon, David Haccoun

Bibliographic record

VenueIET Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChannel state informationQuadrature (astronomy)Adaptive quadratureChannel (broadcasting)Computer scienceAmplitudeAmplitude modulationQuadrature amplitude modulationModulation (music)TelecommunicationsElectronic engineeringControl theory (sociology)PhysicsWirelessFrequency modulationBandwidth (computing)AcousticsEngineeringBit error rateArtificial intelligenceOptics

Abstract

fetched live from OpenAlex

The impact of outdated channel state information (CSI) on the performance of variable‐rate adaptive M ‐ary quadrature amplitude modulation in amplify‐and‐forward (AF) opportunistic relaying systems over time‐variant Rayleigh fading channels is analysed. Two rate‐adaptive modulation techniques are considered. In the first scheme, the relay and the transmission rate are selected according to the CSI at the receiver side. In the second scheme, whereas selection of the relay is based on the receiver's CSI, the transmission rate is selected based on the predicted CSI at the transmitter after the feedback delay. The impact of imperfect CSI prediction on the system performance is also evaluated. For each scheme, analytical expressions are derived for the average spectral efficiency, outage probability and the average bit‐error rate under outdated CSI. Using numerical evaluations the performances of the considered rate‐adaptive modulation schemes are analysed to illustrate the impact of outdated CSI on AF opportunistic relaying systems.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.065
GPT teacher head0.288
Teacher spread0.223 · 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
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

Citations2
Published2013
Admission routes2
Has abstractyes

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