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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-3}$</tex-math></inline-formula>, 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-4}$</tex-math></inline-formula>, 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 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".