MétaCan
Menu
Back to cohort
Record W1002864269 · doi:10.24138/jcomss.v10i1.140

Impact of Uncertain Channel Estimation and Outdated Feedback on the Adaptive M-PSK Modulation

2014· article· en· W1002864269 on OpenAlexaff
Mohamed Lassaad Ammari, François Gagnon

Bibliographic record

VenueJournal of Communications Software and Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsRayleigh fadingComputer scienceModulation (music)Channel (broadcasting)Link adaptationChannel state informationPhase-shift keyingBit error rateFadingSIGNAL (programming language)AlgorithmPilot signalTelecommunicationsStatisticsMathematicsWirelessPhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper investigates an adaptive M-ary phaseshiftkeying (M-PSK) modulation scheme over Rayleigh flatfading channels. The data rate is adapted according to thechannel state. At the receiver, the fading is estimated using pilot symbols. To cancel the channel impact, we correct the received signal by dividing it by the estimated value of the fading. So, we propose to adjust the modulation level by examining the statistics of the corrected signal. In contrast to the previous works on the adaptive M-PSK modulation techniques, our modulation switching protocol takes into account the channel estimation error variance. Moreover, we derive a new closed-form expression for the average bit error rate of the considered system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.270
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Communications Software and SystemsSame topicAdvanced Wireless Network OptimizationFrench-language works237,207