Novel Low-complexity Neural Network Aided Detection for Faster-than-Nyquist (FTN) Signalling in ISI Channel
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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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".