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Turbo Receiver for DS-SS Systems Employing Parity Bit Selected Spreading Codes

2012· article· en· W1997847569 on OpenAlexaff
Alireza Mirzaee, Claude D’Amours

Bibliographic record

VenueIEEE Communications Letters · 2012
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTurbo codeAdditive white Gaussian noiseAlgorithmParity bitComputer scienceBit error rateTurboFadingDecoding methodsTurbo equalizerLow-density parity-check codeElectronic engineeringTelecommunicationsMathematicsBlock codeConcatenated error correction codeChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

In this paper a turbo receiver for direct sequence spread spectrum (DS-SS) systems employing parity bit selected spreading code (SS-PB) is proposed where detection and decoding are performed iteratively for each detected bit in the receiver. In SS-PB systems, parity bits generated by a linear block encoder are used to select a spreading code from a set of orthogonal spreading sequences. Performance evaluation in both additive white Gaussian noise (AWGN) and fading channels show a significant reduction in bit error rates (BER) when a turbo receiver is used in these systems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0070.001
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 head0.087
GPT teacher head0.333
Teacher spread0.246 · 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.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2012
Admission routes1
Has abstractyes

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