Machine Learning based modulation format classification framework for inter-satellite optical wireless communication system (IsOWCS)
Bibliographic record
Abstract
Abstract The exponential growth in demand for high-capacity optical systems has driven the advancement of advanced modulation formats to upgrade transmission capacity and transmission quality. Effective fault diagnosis and self-configuration in inter-satellite optical wireless communication systems (IsOWCS) depend intensely on the generated data. Machine learning (ML) approaches offer promising solutions in evaluating the execution of these networks. In this study, a dataset was created using OptiSystem 18.0. The dataset was composed of various modulation formats such as duobinary, return-to-zero (RZ), non-return-to-zero (NRZ), 33 % RZ, chirped NRZ, vestigial sideband (VSB) NRZ, carrier-suppressed return-to-zero (CSRZ), and VSB CSRZ. The classification of modulation formats has been presented in this study using ML. The dataset was created by varying input power from 0 to 20 dBm and evaluating parameters such as Q factor, input/output signal-to-noise ratio (SNR), power, range, eye closure, amplitude, height, eye opening, output OSNR. Four ML classifiers were used to predict the classification of different modulation formats. Random forest (RF) classifier performed exceptionally well and achieved 100 % accuracy. Moreover, an interactive user-friendly web page was also developed using Anvil for modulation format classification. The proposed research underscores the significance of selecting the appropriate modulation format to optimize the performance and transmission distance of IsOWCS, subsequently enhancing the operation of high-speed optical communication systems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".