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Record W6920662991 · doi:10.60692/nsvfr-71k60

On the Error Probability of Cognitive RF-FSO Relay Networks Over Rayleigh/EW Fading Channels With Primary-Secondary Interference

2020· article· en· W6920662991 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2020
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsCognitive radioFadingRelayTransmission (telecommunications)Channel (broadcasting)Interference (communication)ThroughputDiversity gainDiversity scheme

Abstract

fetched live from OpenAlex

Free space optical (FSO) communication has emerged to provide line of sight connectivity and higher throughput over unlicensed optical spectrums. Cognitive radio (CR), on the other hand, can utilize the radio frequency (RF) spectrum and allow a secondary user (SU) to share the same spectrum with the primary user (PU) as long as the SU does not impose interference on the PU. Owing to the potential of these emerging technologies, to provide full spectrum efficiency, this paper focuses on the mixed CR RF-FSO transmission scheme, where RF communication is employed at one hop followed by the FSO transmission on the other hop in a dual-hop decode-and-forward (DF) configuration. To quantify the performance of the proposed scheme, closed form error probability is derived over Rayleigh/Exponentiated Weibull (EW) fading distributions by considering the statistical and instantaneous feedback channel of the primary network. We also employed an asymptotic analysis to illustrate the diversity gain of the overall system. We believe that the proposed scheme can be applicable to the 5G+ networks where an unlicensed university student connects to the home computer with the aid of an FSO path.

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: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.497

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 science0.0000.000
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 head0.037
GPT teacher head0.199
Teacher spread0.162 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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