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Record W2092958847 · doi:10.1109/rws.2012.6175384

Spectrum sharing technique for cognitive UWB systems over indoor UWB channel

2012· article· en· W2092958847 on OpenAlexaff
F. Sarabchi, Chahé Nerguizian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInterference (communication)Computer scienceMultipath propagationCognitive radioChannel (broadcasting)Electronic engineeringOrthogonal frequency-division multiplexingUltra-widebandWirelessWidebandComputer networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper an interference mitigation algorithm for coexistence of Multiband OFDM ultra wideband systems with other wireless systems is proposed. Since in the previous active interference cancellation methods, the channel effect has not been investigated, this paper focuses on proposing an enhanced spectrum shaping algorithm with aims to generate sufficiently deep spectral notch considering the effect of multipath channel. Moreover, the proposed technique is outfitted with a capability of controlling the depth and width of the created notch. The simulation results show that a notch depth of -85 dB is achievable. Furthermore, it is shown that the proposed method yields significant gains in the performance of the primary user.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.616

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 head0.018
GPT teacher head0.247
Teacher spread0.228 · 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 designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

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