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Capacity-Achieving Distributions for the Discrete-Time Poisson Channel—Part II: Binary Inputs

2014· article· en· W2166346249 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 · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBinary numberPoisson distributionChannel (broadcasting)Current (fluid)Channel capacityMathematicsApplied mathematicsBinary Independence ModelTopology (electrical circuits)Work (physics)Probability mass functionMathematical optimizationComputer scienceProbability distributionStatistical physicsAlgorithmPhysicsTelecommunicationsStatisticsCombinatorics

Abstract

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Discrete-time Poisson (DTP) channels exist in many scenarios including space laser communication systems which operate over long distances and which can be corrupted by reflected and scattered light. Through simulation, binary-input distributions have been observed to be optimal in many cases, however, little analytical work exists on conditions for optimality or the form of optimal signalling. In this second part, the general properties of Part I are extended to the case of DTP channels where binary-inputs are optimal. Necessary and sufficient conditions on the optimality of binary (i.e. two mass point) distributions are presented by leveraging the general properties of DTP capacity-achieving distributions. Closed-form expressions of the capacity-achieving distributions are derived in several important special cases including zero dark current and for high dark current. Numerical results are presented to elucidate the developed analytical work.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.260
Teacher spread0.222 · 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