Signal Space Diversity-Based Distributed RIS-Aided Dual-Hop Mixed RF-FSO Systems
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) is a recently emerged promising technology for beyond-5G (B5G)/6G wireless networks. In this letter, we propose to employ a signal space diversity (SSD) technique to improve the performance of distributed RIS-aided dual-hop mixed radio frequency (RF)-free-space optical (FSO) communication systems. The source-relay, source-RIS, and RIS-relay links are assumed to undergo independent but not identically distributed (i.n.i.d.) Nakagami-$m$fading. The destination node is equipped with multiple FSO apertures and the relay-destination links follow Gamma-Gamma (GG) distributed atmospheric turbulence (AT) model with pointing errors (PEs). Novel approximate closed-form expressions for the system’s symbol error rate (SER) are derived for both the exhaustive RIS-aided (ERA) and opportunistic RIS-aided (ORA) configurations. The numerical results show that using the SSD technique at both the RF and FSO links significantly improves the spectral efficiency and the diversity order of the system considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".