On the Error Probability of Cognitive RF-FSO Relay Networks Over Rayleigh/EW Fading Channels With Primary-Secondary Interference
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".