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Record W2292622906 · doi:10.1109/glocom.2015.7417116

A Novel Spectrum Monitoring Algorithm for OFDM-Based Cognitive Radio Networks

2015· article· en· W2292622906 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2015 IEEE Global Communications Conference (GLOBECOM) · 2015
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsConcordia University
Fundersnot available
KeywordsCognitive radioOrthogonal frequency-division multiplexingComputer scienceInterference (communication)ThroughputTransmission (telecommunications)Electronic engineeringComputer networkTelecommunicationsWirelessEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

-This paper introduces a novel spectrum monitoring algorithm for Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radios so that the primary user activity can be detected during the secondary user transmission. The presented technique fully utilizes the performance of both primary and secondary networks. This is done by sensing the variations in signal power over a number of reserved OFDM sub-carriers so that the reappearance of the primary user is quickly detected and the throughput of both primary and secondary networks are kept high. Both analysis and simulation show that the energy ratio algorithm not only effectively and accurately detects the appearance of the primary user in frequency selective channels, but also offers immunity to the traditional OFDM challenges like the sensitivity of Inter Carrier Interference (ICI) effects.

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.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.087
GPT teacher head0.327
Teacher spread0.240 · 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