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Record W2169720840 · doi:10.1109/icc.2006.255019

A Method for Spectrum Shaping and NBI Suppression in UWB Communications

2006· article· en· W2169720840 on OpenAlexaff
Yue Wang, Xiaodai Dong, I.J. Fair

Bibliographic record

Venue2006 IEEE International Conference on Communications · 2006
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of AlbertaUniversity of Victoria
Fundersnot available
KeywordsNarrowbandSpread spectrumInterference (communication)Electronic engineeringRobustness (evolution)Computer scienceMatched filterPhysicsTelecommunicationsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

This paper investigates spectrum shaping in ultrawideband (UWB) communications in order to introduce spectral nulls to limit interference with narrowband signals. Each transmitted symbol is represented by a monocycle "coded" Gaussian pulse, where use of the monocycle Gaussian pulse ensures that the UWB spectrum mask established by the Federal Communications Commission (FCC) is met and where the codeword is designed to generate a spectral null at the frequency or frequencies being used by existing narrowband devices. Signals obtained with different spectrum shapings (e.g., Butterworth, Chebyshev, elliptical) and by introducing nulls at multiple interference frequency bands are discussed. The performance of a monocycle coded UWB system with a spectral null in the presence of narroband interference (NBI) using single carrier block transmission with frequency domain equalization (SC-FDE) is simulated and compared with that of an uncoded SC-FDE UWB system using a single Gaussian monocycle. Our results show that NBI can be effectively suppressed by transmitting and matched filtering the pulse with a spectral null at the interference frequency, therefore improving the robustness of SC-FDE UWB to NBI.

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 score0.874
Threshold uncertainty score0.930

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 science0.0030.000
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 head0.088
GPT teacher head0.357
Teacher spread0.268 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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