Precise Performance Analysis of Dual-Hop Mixed RF/Unified-FSO DF Relaying With Heterodyne Detection and Two IM-DD Channel Models
Bibliographic record
Abstract
This paper provides precise performance analysis of the dual-hop mixed radio frequency (RF)/unified free space optical (FSO) decode-and-forward (DF) relaying system, in which the heterodyne detection and the intensity modulation-direct detection (IM-DD) are taken into account for FSO detection. To this end, we derive closed-form expressions for the outage probability, average bit error rate (BER), and the ergodic channel capacity of this system. In this analysis, we utilize, for the first time as per our knowledge, a precise channel capacity result for the IM-DD channel. Moreover, this is the first time that not only the (IM-DD input-independent) but also the (IM-DD cost-dependent) additive white Gaussian noise (AWGN) channel is considered in such system analyses. Additionally in this study, we assume that the first hop (RF link) follows Nakagami-mfading, and the second hop (FSO link) follows Málaga (M) turbulence with pointing errors. These fading and turbulence models contain other models (such as Rayleigh and Gamma-Gamma) as special cases, thus, our analyses can be seen as a generalized one from the RF and FSO fading models point of view. Also, in BER derivation, we assume that the modulation schemes in the two hops can be different, since not all modulation schemes are suitable for IM-DD FSO links. In addition, the system performance is investigated asymptotically at high signal to noise ratios. This investigation leads to new nonreported coding gain and diversity order analyses of such system. Interestingly, we find that in the FSO hop, at high transmitted powers, all the considered FSO detectors result in the same diversity order. Furthermore, we provide simulation results that verify the accuracy of the obtained analytical and asymptotic expressions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".