Machine Learning Aided Demodulator in MISO Beamforming Systems
Why this work is in the frame
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Bibliographic record
Abstract
In this study, a machine learning (ML) aided demodulator was designed for equalization and demodulation of the signal at the receiver in a multiple-input single-output (MISO) beamforming system. To observe the accuracy of MLaided demodulator in the presence of Rayleigh channel; XGBoost, LightGBM, and Linear Support Vector Machine (SVM) algorithms are used. Several scenarios were discussed where the number of antennas, signal-to-noise ratio (SNR), channel perfection, and channel information availability changed. As a result of 72 different measurements, it was observed that Linear SVM operates at slightly lower bit error rate (BER) levels among 3 algorithms. However, the SVM has higher time complexity and could not provide a significant advantage over other algorithms. Therefore, the use of the LightGBM algorithm, with a very low amount of BER sacrifice, greatly reduces time complexity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it