Rapid prototyping and validation on an SDR platform of a low-cost hybrid ML SNR estimator over time-varying SIMO channels
Bibliographic record
Abstract
An application of a rapid prototyping method on an SDR platform is presented. A novel low-cost hybrid maximum likelihood (ML)signal-to-noise ratio (SNR) estimator over single-input multiple-output (SIMO) time-varying fading channels, is implemented, tested, and validated. The approach adopted to achieve this work is modelbased. It requires no coding since it is relies on MAT-LAB/Simulink and Xilinx Sytsem Generator (XSG) tools that offer a graphical user interface (GUI) and a drag- and-drop method. These software tools allow the creation of a design architecture for the estimator which is then translated into hardware description language (HDL) and implemented on the targeted SDR. Nutaq's PicoSDR2X2 platform and Anite's EB Propsim channel emulator define our experimental environment. The experimental results obtained in real-time and over-the-air corroborate those previously generated off-line by MATLAB.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".