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Record W1967298188 · doi:10.1109/vtcfall.2014.6966107

Performance Analysis for RUB-Based Cognitive Radio Network with Cooperative Beam Selection

2014· article· en· W1967298188 on OpenAlexaff
Tianqing Wu, Hong‐Chuan Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsThroughputUSableCognitive radioTransmitterSelection (genetic algorithm)Computer scienceOutage probabilityBeam (structure)Primary (astronomy)Base stationComputer networkTelecommunicationsEngineeringPhysicsStructural engineeringFadingDecoding methodsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper studies the performance of a RUB-based cognitive radio (CR) network, which coexists with a primary network with single primary transmitter (PT) and single primary user (PU). It is assumed that the PU can control the availability of each beam of the secondary base station (SBS) over a feedback link. The PU only needs to feed back to the SBS the index of the usable beams, which lead to the received SINR at the PU larger than a predefined beam selection threshold, and the outage probability of the primary system can be limited to a tolerable level. The SBS then selects one from the usable beams to achieve the largest throughput of the secondary system. We derive the accurate upper bound of the outage probability of the primary system and the exact throughput of the secondary system. Numerical examples show that to avoid the burst increase of the outage probability of the primary system, as well as achieve the maximal throughput of the secondary system, the beam selection threshold should be equal to the outage threshold of the primary system.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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