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Record W1520059214 · doi:10.1109/itsc.2003.1252742

A robust chaotic spread spectrum inter-vehicle communication scheme for ITS

2004· article· en· W1520059214 on OpenAlexaff
Surendran K. Shanmugam, Henry Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicChaos control and synchronization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSpread spectrumChaoticComputer scienceMultipath propagationDemodulationSynchronization (alternating current)FadingCommunications systemDirect-sequence spread spectrumErgodic theoryElectronic engineeringFrequency-hopping spread spectrumTelecommunicationsEngineeringCode division multiple accessMathematicsDecoding methodsChannel (broadcasting)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, an effective chaos based non-coherent spread spectrum communication scheme is proposed for inter-vehicle communication in intelligent transportation systems (ITS). The proposed spread spectrum scheme modulates the data to be broadcasted into the bifurcation parameter of the chaotic system and therefore the transmitted signal is broadband. By exploiting the ergodic property of chaotic signals, a simple non-coherent receiver is developed to demodulate the received signal. The new spread spectrum scheme can be implemented using simple hardware and with low manufacturing cost, which is one of the important considerations for communications in an ITS. It is shown here that the proposed chaotic spread spectrum system is highly robust against synchronization error and rapid multipath fading caused by vehicular mobility through computer simulations. Performance analysis illustrate the superior communication performance of the proposed chaotic spread spectrum over the conventional direct sequence spread spectrum scheme for collaborative driving in an ITS.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.441

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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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 designTheoretical or conceptual
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

Citations4
Published2004
Admission routes1
Has abstractyes

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