New Algorithm for a Fixed Right Shift Code to Support Different Quality of Services in Smart and Sustainable Optical Networks
Bibliographic record
Abstract
Smart and sustainable cities require a network that can ensure many exchanges of information. In this sense, the deployment of optical fiber seems essential in order to guarantee urban interconnection. In this work, a new algorithm for the right shift code to be used in optical fiber communication (OFC) is proposed. The algorithm was designed to support different quality of services (audio, video, and data) for smart cities. This algorithm was based on varying the code weight for a certain number of users and transmitting a service to them. This algorithm was used for a spectral amplitude-coding optical code division multiple-access (SAC-OCDMA) system. Two detection scheme approaches were used, which are the single photodiode (SPD) detection technique and the direct detection (DD) technique. The performance was analytically studied for the proposed system in terms of BER, SNR, and the maximum number of users. The obtained results, when our system used SPD, showed an improvement in the number of users, about 2.5% at 622 Mbps and 1.4% at 1.25 Gbps and 2.5 Gbps. It can support a number of users up to 151 for video services at BER = 10−12, 300 for audio services with BER = 10−3, and 162 for data services at BER = 10−9 at a data rate of 1.25 Gbps. Furthermore, the system was simulated and showed transmission distances of 30 km at 1.25 Gbps and 6 km at 2.5 Gbps for video services, 80 km at 1.25 Gbps and 30 km at 2.5 Gbps for audio services, and 44 km at 1.25 Gbps and 13 km at 2.5 Gbps for data services. Our proposed system is suggested to be implemented in a fixed fifth-generation passive optical network (F5G PON).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".