Low Complexity Soft-Output Faster-than-Nyquist Detector Based on Semidefinite Relaxation
Bibliographic record
Abstract
Faster than Nyquist (FTN) signaling is an attractive transmission technique that is capable of improving the spectral efficiency with additional detection complexity at the receiver. Semidefinite relaxation (SDR) based FTN detectors are appealing as they provide good performance with linear decoding complexity. In this paper, we propose a soft-output semidefinite relaxation (soSDR) based FTN detector which has a similar polynomial complexity order when compared to its counterpart that only produces hard-output decisions. The main complexity reduction lies in re-using the candidate sequences generated in the Gaussian randomization (GR) step to produce reliable soft-output values, which approximate the calculation of the log-likelihood ratio (LLR) inputs for the channel decoder. The effectiveness of the proposed soSDR algorithm is evaluated using polar codes with successive cancellation decoding (SCD) through simulations, and its performance is compared against the state-of-the-art techniques from the literature. Simulation results show that the proposed soSDR algorithm provides reliable LLR values and strikes a good balance between detection complexity and bit error rate (BER) performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".