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Record W1988661175 · doi:10.1155/2012/348982

Analysis and Mitigation of the Narrowband Interference Impact on IR‐UWB Communication Systems

2012· article· en· W1988661175 on OpenAlexaff
Ehab M. Shaheen, M. El-Tanany

Bibliographic record

VenueJournal of Electrical and Computer Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsCarleton University
Fundersnot available
KeywordsNarrowbandInterference (communication)Electronic engineeringComputer scienceAdjacent-channel interferenceEngineeringTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

The impact of narrowband interference signals on impulse radio ultrawideband (UWB) communication systems has been investigated. A closed form expression for the bit error rate performance of UWB communication system in a Log‐normal flat fading channel under such impact is evaluated. The actual UWB channel model is known as a multipath fading channel; however flat fading channel model can be considered with some of the UWB wireless applications such as UWB wireless sensor networks which are characterized by size and energy constraints. Thus, a simple and low‐cost one‐finger Rake receiver can be used with such wireless systems. It was proven that UWB systems unavoidably suffer from the interference caused by the coexisting systems due to the restraint on their transmission power levels. To this end, we propose an interference canceller scheme which is capable of suppressing the impact of such interference and enhancing the performance of UWB communication systems. The interference canceller scheme performance is also investigated in various scenarios of operation such as the presence of multiple narrowband interference signals, symbol timing error, and a comparison with a notch filter‐based case.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2012
Admission routes1
Has abstractyes

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