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Impact of Beacon Misdetection on Aggregate Interference for Hybrid Underlay-Interweave Networks

2013· article· en· W2141762353 on OpenAlexaff
Sachitha Kusaladharma, Chintha Tellambura

Bibliographic record

VenueIEEE Communications Letters · 2013
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUnderlayPath lossComputer scienceInterference (communication)Rayleigh fadingComputer networkNode (physics)Topology (electrical circuits)TelecommunicationsCognitive radioFadingWirelessSignal-to-noise ratio (imaging)MathematicsPhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

The impact of beacon misdetection on the aggregate interference from a hybrid underlay-interweave network is analyzed, for a Poisson field of cognitive radio (CR) nodes distributed over an annular region. This network consists of two types of nodes: underlay, and interweave. The underlay nodes are allowed to transmit anytime, whereas the interweave nodes must first sense an out-of-band beacon. When this sensing is erroneous, interweave node transmissions increase the interference. We analyze the interference statistics by deriving the exact moment generating function, the mean, and the outage probability of the primary receiver, for path loss and Rayleigh fading. Our analysis suggests that hybrid underlay-interweave CR systems are more suitable for areas with low path loss exponents such as rural/suburban environments.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.654

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 science0.0010.000
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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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 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

Citations16
Published2013
Admission routes1
Has abstractyes

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