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Record W2316613882 · doi:10.1109/tcomm.2016.2550524

Error Probability Analysis and Applications of Amplitude-Coherent Detection in Flat Rayleigh Fading Channels

2016· article· en· W2316613882 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

VenueIEEE Transactions on Communications · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsDetectorFadingRayleigh fadingAlgorithmMultipath propagationElectronic engineeringMonte Carlo methodDetection theoryMathematicsProbability density functionComputer scienceDiversity combiningQuadrature amplitude modulationBit error rateStatisticsChannel (broadcasting)TelecommunicationsEngineeringDecoding methods

Abstract

fetched live from OpenAlex

This paper presents the detector design and symbol error rate (SER) analysis of M-ary amplitude shift keying over multipath fading channels using amplitude-coherent detection (ACD). The optimum detector is derived using the maximum likelihood criterion, and then it is used to derive two efficient low-complexity suboptimal detectors. The probability distribution function of the decision variables and the SER of the two suboptimal detectors are expressed using simple closed-form analytical formulas when single receiving antenna is used. The SER with receiver diversity is obtained using Monte Carlo simulation. The obtained analytical and simulation results reveal that ACD can provide reliable SER as compared with noncoherent detection. Moreover, we present an efficient blind channel estimation algorithm using ACD and hybrid modulation frame structures.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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