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Record W2103020432 · doi:10.1109/pimrc.2008.4699817

Soft output detector for convolutionally encoded Parity Bit selected Multicarrier Direct-Sequence Spread Spectrum system

2008· article· en· W2103020432 on OpenAlexaff
Alireza Mirzaee, Claude D’Amours

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsParity bitViterbi decoderComputer scienceBit error rateAlgorithmDetectorConvolutional codeSpread spectrumViterbi algorithmDecoding methodsElectronic engineeringChannel (broadcasting)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper we investigate the performance of the Parity Bit selected Multicarrier Direct Sequence Spread Spectrum (PB-MC-DS-SS) system using Log Likelihood Ratio (LLR). In this system information bits are convolutionally encoded prior to being used in the parity bit selected MC-DS-SS system. Reliability of the detected bits in the receiver is calculated in the form of LLRs and then used as the input of a soft input Viterbi decoder. The performance of the proposed system is compared to conventional coded multicarrier spread spectrum systems where no parity bit selected spreading sequence is used.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.952
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.055
GPT teacher head0.282
Teacher spread0.227 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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