Polar Coded Faster-than-Nyquist (FTN) Signaling with Symbol-by-Symbol\n Detection
Bibliographic record
Abstract
Reduced complexity faster-than-Nyquist (FTN) signaling systems are gaining\nincreased attention as they provide improved bandwidth utilization for an\nacceptable level of detection complexity. In order to have a better\nunderstanding of the tradeoff between performance and complexity of the reduced\ncomplexity FTN detection techniques, it is necessary to study these techniques\nin the presence of channel coding. In this paper, we investigate the\nperformance a polar coded FTN system which uses a reduced complexity FTN\ndetection, namely, the recently proposed successive symbol-by-symbol with\ngo-backK sequence estimation (SSSgbKSE) technique. Simulations are performed\nfor various intersymbol-interference (ISI) levels and for various go-back-K\nvalues. Bit error rate (BER) performance of Bahl-Cocke-Jelinek-Raviv (BCJR)\ndetection and SSSgbKSE detection techniques are studied for both uncoded and\npolar coded systems. Simulation results reveal that polar codes can compensate\nsome of the performance loss incurred in the reduced complexity SSSgbKSE\ntechnique and assist in closing the performance gap between BCJR and SSSgbKSE\ndetection algorithms.\n
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How this classification was reachedexpand
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".