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Record W2963740087 · doi:10.1109/access.2018.2870562

Real-Time Spectrum Sniffer for Cognitive Radio Based on Rotman Lens Spectrum Decomposer

2018· article· en· W2963740087 on OpenAlexaff
Xiaoyi Wang, Alireza Akbarzadeh, Lianfeng Zou, Christophe Caloz

Bibliographic record

VenueIEEE Access · 2018
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBandwidth (computing)Cognitive radioComputer scienceDecomposerExtremely high frequencyRadio spectrumSpectrum (functional analysis)Electronic engineeringTelecommunicationsWirelessPhysicsEngineering

Abstract

fetched live from OpenAlex

We introduce the concept of an energy-detection Rotman-lens spectrum decomposer (RLSD) real-time spectrum-sniffer (RTSS) for cognitive radio. Compared to a previously existing RTSS, the RLSD-RTSS offers the advantages of being 1) Based an a simpler and lower-cost purely passive structure, 2) Easier to design and easily amenable to tunability, 3) Of much broader bandwidth, and 4) Of accommodating more channels. The electrical size of the device is electrically larger, but perfectly acceptable in the millimeter-wave frequency range. The proposed RLSD-RTSS is demonstrated theoretically and experimentally, and is shown to support tunability in terms of both bandwidth-resolution and operation band. Given its unique features, this device may find wide applications in 5G UHD and 3-D video systems.

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 categoriesMeta-epidemiology (narrow)
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 score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.026
GPT teacher head0.294
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.

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

Citations11
Published2018
Admission routes1
Has abstractyes

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