Neural network implementation of a fade countermeasure controller for a VSAT link
Bibliographic record
Abstract
Abstract In this paper, an algorithm and its practical implementation to activate adaptive modulation as a fade countermeasure (FCM) is presented. Characteristics of the algorithm are derived from the propagation studies and the algorithm uses a simple four parameter model. A neural network architecture was used to implement the decision making block of the controller. The algorithm has been implemented on a fixed point DSP. An experimental set‐up with an emulated Ka‐band satellite link and a terrestrial return path connection has been used with previously recorded propagation data for experimental verification and performance analysis. Performance of the implemented FCM system is compared with that of fixed, non‐adaptive systems. Over concatenated rain events, the adaptive system yields better throughput at or below a given BER than any fixed mode system and only marginally worse BER availability than the most robust scheme in the system. The FCM is thus essential if throughput is of importance as well as availability. The added complexity of the FCM system is not great by contemporary technology standards and is, in authors' opinion, well worth the investment. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".