Low Complexity Lookup Table Aided Soft Output Semidefinite Relaxation Based Faster-than-Nyquist Signaling Detector
Bibliographic record
Abstract
Spectrum scarcity necessitates innovative, spectral-efficient strategies to meet the ever-growing demand for high data rates. Faster-than-Nyquist (FTN) signaling emerges as a compelling spectral-efficient transmission method that pushes transmit data symbols beyond the Nyquist limit, offering en-hanced spectral efficiency (SE). While FTN signaling maintains SE with the same energy and bandwidth as the Nyquist signaling, it introduces increased complexity, particularly at higher modulation levels. This complexity predominantly arises from the detection process, which seeks to mitigate the intentional intersymbol interference generated by FTN signaling. Another challenge involves the generation of reliable log-likelihood ratios (LLRs) vital for soft channel decoders. In this study, we introduce a lookup table (LUT) aided soft output semidefinite relaxation (soSDR) based sub-optimal FTN detector, which can be extended to higher modulation levels. This detector possesses polyno-mial computational complexity, given the negligible complexity associated with soft value generation. Our study assesses the performance of this soft output detector against that of the optimal FTN detector, Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm as the benchmark. The likelihood values produced by our LUT aided semidefinite relaxation (SDR) based FTN signaling detector show promising viability in coded scenario.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".