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Record W2125192302 · doi:10.1002/wcm.1214

Channel capacity and bit error rate optimization of the ultra‐wide bandwidth transmitted‐reference receiver

2011· article· en· W2125192302 on OpenAlexaff
Shuyi Wang, Yunfei Chen, Mark S. Leeson, Norman C. Beaulieu

Bibliographic record

VenueWireless Communications and Mobile Computing · 2011
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceBit error rateBandwidth (computing)Channel (broadcasting)Channel capacityInterference (communication)Bit (key)Electronic engineeringTelecommunicationsAlgorithmComputer network

Abstract

fetched live from OpenAlex

ABSTRACT The channel capacity and the bit error rate of ultra‐wide bandwidth transmitted‐reference systems are optimized with respect to time delay between the reference and the adjacent data pulses. Approximate and theoretical expressions for the signal‐to‐interference‐plus‐noise ratio are derived. Numerical results show that optimizing the receiver can provide a significant capacity improvement of up to 2.2 bits/s/GHz and a bit error rate performance gain of up to 2 dB in effective signal‐to‐noise ratio. Copyright © 2011 John Wiley & Sons, Ltd.

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 score0.637
Threshold uncertainty score0.522

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.220
Teacher spread0.186 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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