WLC28-5: Impact of Frequency Offset and Timing Offset on the Performance of SC-FDE UWB
Bibliographic record
Abstract
We investigate in this paper the effect of carrier frequency offset (CFO) and sampling timing offset (STO) on the performance of single carrier block transmission with frequency domain equalization (SC-FDE) over ultra-wideband (UWB) channels. Signal-to-interference-plus-noise ratio (SINR) of SC-FDE UWB in the presence of CFO is analyzed and compared with that of OFDM, where OFDM is shown to be more sensitive to CFO. We also study the effect of STO on the performance of SC-FDE. Two forms of STO are considered, i.e., a shifted sampling timing instant other than the optimal timing point and a distorted sampling rate other than the symbol rate. Our results show that the performance of SC-FDE is rather channel dependent when sampling at a non-optimal time instant within one symbol duration, where the energy of the sampled equivalent channel at the sampling point determines its BER performance. Moreover, an SC-FDE system is fairly sensitive to a distorted sampling rate at the receiver, where severe performance degradation can occur when the received signal is sampled at a sampling rate rather than symbol rate.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".