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Record W2043069670 · doi:10.1145/2512840.2512869

Performances of trigonometric chirp spread spectrum modulation in AWGN & rayleigh channels

2013· article· en· W2043069670 on OpenAlexafffund
Quan Wang, Jin Jiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdditive white Gaussian noiseChirp spread spectrumChirpModulation (music)Frequency modulationSpread spectrumSpectrum (functional analysis)Electronic engineeringPhysicsMathematicsComputer scienceTelecommunicationsDirect-sequence spread spectrumAcousticsBandwidth (computing)White noiseOpticsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

A linear CSS (Chirp Spread Spectrum) system generally requires its time-bandwidth product to be 60 or more in order to achieve satisfactory orthogonality between different chirp signals. Thus, a linear CSS system occupies very wide bandwidth (e.g. 60MHz or higher) for 1μs chirp duration. To improve the orthogonality and to reduce the bandwidth requirements, this paper explores two kinds of non-linear chirps: power function chirp and trigonometric chirp. It is concluded that a pair of trigonometric chirp can be made orthogonal under some mild conditions, and the orthogonality shows periodic characteristic. In other words, the CSS system based on trigonometric chirp could result in significant reduction in required bandwidth as compared to that of linear chirps for the same channel performance. Further, extensive simulations have been carried out to illustrate the BER vs. Eb/No performance in Gaussian and Rayleigh channels. The results show that the performance of the CSS system based on trigonometric chirp outperforms that of linear chirp based systems, especially when the time-bandwidth product of chirp is small (e.g. 30sHz or less).

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.276
Teacher spread0.240 · 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
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
Published2013
Admission routes2
Has abstractyes

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