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Record W2343651235 · doi:10.1109/tvt.2016.2518988

Underlay Interference Analysis of Power Control and Receiver Association Schemes

2016· article· en· W2343651235 on OpenAlexaff
Sachitha Kusaladharma, Prasanna Herath, Chintha Tellambura

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2016
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransmitterUnderlayRayleigh fadingTransmitter power outputPower controlPath lossCognitive radioFadingNode (physics)Interference (communication)Computer scienceElectronic engineeringTopology (electrical circuits)MathematicsTelecommunicationsEngineeringPower (physics)Electrical engineeringSignal-to-noise ratio (imaging)WirelessPhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, we present a precise comprehensive analysis of the aggregate interference I generated from an underlay network of cognitive radio (CR) nodes employing several transmit power control and receiver association schemes. Importantly, we consider spatial randomness by modeling CR transmitter nodes and receiver nodes as two independent Poisson point processes (PPPs). For the cognitive nodes, we investigate receiver association based on the distance or the instantaneous received power and power control based on the maximum possible transmitter-receiver distance, fixed or location-dependent cutoff power levels, feedback from the primary system, or the maximum number of available receivers. For each of these schemes, the exact moment generating function (mgf) and mean of aggregate I power are derived for links with Rayleigh fading and exponential path loss. The resulting primary outage and the probability of secondary transmitter cutoff are also derived. Numerical results show that the secondary power thresholds and node densities significantly affect the aggregate I, the primary receiver (PR) outage, and the secondary transmitter cutoff arising from the different schemes.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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 designOther design
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

Citations34
Published2016
Admission routes1
Has abstractyes

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