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Record W2038036388 · doi:10.1109/cwit.2013.6621618

Systematic raptor codes for atmospheric optical channels

2013· article· en· W2038036388 on OpenAlexaff
Linyan Liu, Majid Safari, Steve Hranilovic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceFree-space optical communicationRaptor codeKeyingTransmitterRedundancy (engineering)SynchronizingNetwork packetOptical communicationBit error rateReal-time computingElectronic engineeringForward error correctionChannel (broadcasting)Computer networkTelecommunicationsDecoding methodsTransmission (telecommunications)EngineeringConcatenated error correction codeBlock code

Abstract

fetched live from OpenAlex

In this paper, a rate-adaptive free-space optical (FSO) communication system using systematic Raptor codes is investigated based on experimental data measured over a 1.87 km terrestrial FSO link in different weather conditions. Using Raptor codes, the FSO communication system can adapt to the channel gain fluctuations caused by atmospheric turbulence and/or adverse weather conditions. To alleviate the burden of synchronizing between transmitter and receiver, messages are packetized and sent in sequence. For each packet, a cyclic-redundancy code (CRC) is added resulting in an effective packet erasure channel (PEC). Using experimental data and on-off-keying (OOK) modulation, the performance of the rate-adaptive FSO system is evaluated at different signaling rates. While an uncoded FSO system fails to efficiently operate during adverse weather conditions, the rate-adaptive technique provides reliable FSO communication under clear and rainy weather conditions.

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: Methods
Teacher disagreement score0.959
Threshold uncertainty score0.423

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 science0.0010.000
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 head0.015
GPT teacher head0.252
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 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 routes1
Has abstractyes

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