Soft-Switching Hybrid FSO/RF Links Using Short-Length Raptor Codes: Design and Implementation
Bibliographic record
Abstract
Free-space optical (FSO) links offer gigabit per second data rates and low system complexity, but suffer from atmospheric loss due to fog and scintillation. Radio-frequency (RF) links have lower data rates, but are relatively insensitive to weather. Hybrid FSO/RF links combine the advantages of both links. Currently, selection or "hard-switching" is performed between FSO or RF links depending on feedback from the receiver. This technique is inefficient since only one medium is used at a time. In this paper, we develop a "soft-switching" scheme for hybrid FSO/RF links using short-length Raptor codes. Raptor encoded packets are sent simultaneously on both links and the code adapts to the conditions on either link with very limited feedback. A set of short-length Raptor codes (κ = 16 to 1024) are presented which are amenable to highspeed implementation. A practical Raptor encoder and decoder are implemented in an FPGA and shown to support a 714 Mbps data rate with a 97 mW power consumption and 26360 gate circuit scale. The performance of the switching algorithms is simulated in a realistic channel model based on climate data. For a 1 Gbps FSO link combined with a 96 Mbps WiMAX RF link, an average rate of over 472 Mbps is achieved using the implemented Raptor code while hard-switching techniques achieved 112 Mbps on average.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".