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Record W3184117287 · doi:10.5539/nct.v6n1p34

There is no Theoretical Limit for Noise Reduction in Digital Communications

2021· article· en· W3184117287 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNetwork and Communication Technologies · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStatistical Mechanics and Entropy
Canadian institutionsnot available
Fundersnot available
KeywordsGaussian noiseNoisy-channel coding theoremMathematicsEntropy (arrow of time)Information theoryStatistical physicsUpper and lower boundsQuadrature amplitude modulationNoise reductionQAMStatisticsAlgorithmMathematical analysisComputer scienceBit error ratePhysicsQuantum mechanicsArtificial intelligenceDecoding methods

Abstract

fetched live from OpenAlex

Shannon entropy is a basic characteristic of communications from energetic point of view. Entropy has been expressed as a function of signal to noise ratio, and lower bound for entropy has been investigated. We prove that finite nonzero bound does not exist, therefore in case of M-QAM modulation, there is no theoretical limit for reduction of the effect of noise. In our investigations, averaging is considered, exploiting the zero expected value of the Gaussian noise. Index Terms—Shannon entropy, probability of successful communication, bit error rate, signal to noise ratio, bound for noise reduction.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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