Novel Low-complexity Neural Network Aided Detection for Faster-than-Nyquist (FTN) Signalling in ISI Channel
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Bibliographic record
Abstract
This thesis studies the application of NN's to Viterbi-detection of FTN-signals in ISI-channel. We propose a novel low-complexity neural-network for calculating branch metrics, and we explore its suitability for FTN-signalling with channel-uncertainty. We compare the proposed-network, called the-MetricNet (MetNet), to a benchmark neural-network-based-technique for metric calculation, the ViterbiNet, originally designed for ISI-channels. The results confirm that the-MetNet outperforms ViterbiNet, with two-orders-of magnitude lower-complexity, and is more-resilient to channel-uncertainty than traditional-Viterbi-detector, which uses Euclidean-distance for metric-calculations. We show that the-MetNet exhibits robustness to being trained at mismatched SNR-values and FTN-pulse-acceleration-factors, meaning that the number of trained-models required can be significantly-reduced. Additionally, the-results show that the-proposed-MetNet remains a favorable-alternative at higher-levels of channel uncertainties. The-results reflect that we can generalize the-MetNet to work with different channel-models defined by different decaying-factors. Finally, we show-that we succeed in achieving a bandwidth-efficiency gain of 33% due to FTN by using the-MetNet in presence of channel-uncertainty.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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