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Record W2106422674 · doi:10.1109/vtcf.2006.268

Impact of User Collaboration on the Performance of Sensing-Based Opportunistic Spectrum Access

2006· article· en· W2106422674 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

VenueIEEE Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceSpectrum managementScarcityService providerComputer networkTelecommunicationsRadio spectrumService (business)Computer securityCognitive radioBusinessWireless

Abstract

fetched live from OpenAlex

Spectrum scarcity is becoming a major issue for service providers interested in either deploying new services or enhancing the capacity for existing applications. On the other hand, recent measurements suggest that many portions of the licensed (primary) spectrum remain unused for significant periods of time. This has led the regulatory bodies to consider opening up under-utilized licensed frequency bands for opportunistic access by unlicensed (secondary) users. Among different options, sensing-based access incurs a very low infrastructure cost and is backward-compatible with the legacy primary systems. In this paper we investigate the effect of user collaboration on sensing- based secondary access in terms of critical parameters such as the spectrum utilization, the required signal-to-noise-ratio and the detection time. As suggested by our analysis and simulation results, collaboration may improve the opportunistic spectrum access significantly.

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)0.000
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 score0.650
Threshold uncertainty score0.555

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 studies0.0000.000
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 head0.019
GPT teacher head0.261
Teacher spread0.243 · 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