MétaCan
Menu
Back to cohort
Record W2070997326 · doi:10.1109/vtcfall.2014.6965860

Aggregate Interference Analysis for Interweave Cognitive Networks

2014· article· en· W2070997326 on OpenAlexaff
S. Kusaladharma, Prasanna Herath, Chintha Tellambura

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNakagami distributionFadingCognitive radioInterference (communication)Computer sciencePath lossMoment-generating functionPoisson distributionTransmitter power outputTopology (electrical circuits)Node (physics)Shadow mappingAggregate (composite)TelecommunicationsAlgorithmComputer networkRandom variableMathematicsTransmitterStatisticsWirelessPhysics

Abstract

fetched live from OpenAlex

This paper investigates the aggregate interference from interweave cognitive secondary nodes spatially distributed in a finite Poisson field. These secondary nodes sense an out-of-band beacon to initiate their transmissions, which can be concurrent with those of the primary system if a sensing error is made. The resulting aggregate interference is analyzed in this paper. For this purpose, general Nakagami-m fading and path-loss are assumed for all relevant channels. Moreover, we incorporate random secondary node transmit powers with any probability distribution. The analysis includes the exact moment generating function (MGF) of the aggregate interference along with the exact outage probability of the primary system. Furthermore, we develop a simple MGF approximation which is valid for severely fading channels and for lower beacon reception threshold to beacon transmit power ratios. Finally, we show that a lower fading severity significantly improves the diversity order of the PR due to more accurate spectrum sensing by the secondary nodes.

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: Methods · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.623

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 science0.0000.000
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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
GenreMethods

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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicCognitive Radio Networks and Spectrum SensingFrench-language works237,207