Robust signal‐to‐noise ratio and noise variance estimation for single carrier frequency domain equalisation ultra‐wideband wireless systems
Bibliographic record
Abstract
In block‐mode transmission, single carrier frequency domain equalisation‐based ultra‐wide‐band technique is a promising physical‐layer candidate scheme. Here, the authors address the noise variance estimation problem for signal‐to‐noise ratio estimation in such systems. Our investigations focus on the estimation schemes both robust to the pilot sequence and the channel type. First, the authors, respectively, define the correlated noise samples and the uncorrelated ones, where the difference signals or the sum signals of two adjacent received pilot signals are included. Then, with the help of the Cramer–Rao lower bound (CRLB) theorem, the authors either use the correlated or the uncorrelated samples to estimate the noise variance based on the difference signals and the sum signals, respectively, regardless of the pilot sequences. The corresponding CRLBs are also given and analysed. Finally, under either the correlated or the uncorrelated noise samples, the authors propose to linearly combine the estimator of the difference signals with that of the sum signals where the weight coefficients are optimised. Since the proposed two combined estimators are not only robust to the pilot sequence but also automatically adapt the weights to the channel condition, they can significantly outperform the existing estimators.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".