Joint Superimposed Pilot-Aided Channel Estimation and Data Detection for FTN Signaling Over Doubly-Selective Channels
Bibliographic record
Abstract
Faster-than-Nyquist (FTN) signaling and superimposed pilot (SP) are known techniques to improve the spectral efficiency (SE). This paper proposes an innovative SP-aided channel estimation method for FTN signaling over doubly-selective channels. We adopt a basis expansion model (BEM) to avoid complex channel tracking, and we propose a SP-aided frame structure that eliminates the overhead of multiplexed pilots (MPs). We additionally propose two detection methods: (1) an SP-aided separate channel estimation and data detection (SCEDD) method performing a single channel estimation followed by iterative data detection via a turbo equalizer, and (2) an SP-aided joint channel estimation and data detection (JCEDD) method, which extends the SCEDD by updating the channel estimate in each turbo equalization iteration. At equivalent SE and a high fading rate on the order of$10^{-3}$, our simulations show that SP-aided SCEDD method outperforms MP-aided techniques in both the mean square error (MSE) and bit error rate (BER), while the SP-aided JCEDD method delivers remarkable performance, where reference approaches fail to track rapid channel variations. At a very low fading rate on the order of$10^{-4}$, the SP-aided JCEDD algorithm enhances the MSE by over 6 dB and 2 dB compared to the MP-aided frequency domain equalization (FDE) and time domain equalization (TDE) methods, respectively. In terms of BER, the JCEDD provides over 3 dB enhancements compared to MP-aided FDE, while remaining competitive with MP-aided TDE, showing only less than 0.5 dB degradation.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".