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Record W2132585254 · doi:10.1109/icassp.2006.1661016

Efficient Chaotic Spreading Codes for DS-UWB Communication System

2006· article· en· W2132585254 on OpenAlexaff
Surendran K. Shanmugam, Henry Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAdditive white Gaussian noiseComputer scienceFadingMultipath propagationMultipath interferenceNarrowbandElectronic engineeringSpread spectrumUltra-widebandInterference (communication)TelecommunicationsCode division multiple accessDecoding methodsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Ultra wideband (UWB) technology is characterized by transmitting extremely short duration radio pulses. To improve its multiple access (MA) capability, UWB technology can be combined with traditional spread spectrum (SS) techniques. Existing spreading codes are only optimal in additive white Gaussian noise (AWGN) and their performance degrades in multipath fading and narrow band interference. In this paper, we propose the use of spreading codes obtained using a novel design methodology based on genetic programming (GP) and DNA computation for DS-UWB communications. The spreading codes obtained by this novel design methodology performs better than traditional spreading codes in both AWGN and multipath fading. In addition, spreading codes with desired spectral characteristics could be designed to minimize the mutual interference between the DS-UWB and co-existing narrowband systems. The proposed design methodology is attractive for spreading code in terms of performance and flexibility to design spreading codes for a specific design criteria

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: none
Teacher disagreement score0.585
Threshold uncertainty score0.399

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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