Closed-Form Expressions for the Exact Cramér–Rao Bounds of Timing Recovery Estimators From BPSK, MSK and Square-QAM Transmissions
Bibliographic record
Abstract
In this paper, we derive for the first time analytical expressions for the exact Cramér–Rao lower bounds (CRLB) for symbol timing recovery of binary phase shift keying (BPSK), minimum shift keying (MSK), and square QAM-modulated signals. It is assumed that the transmitted data are completely unknown at the receiver and that the shaping pulse verifies the first Nyquist criterion. Moreover the carrier phase and frequency are considered as unknown nuisance parameters. The time delay remains constant over the observation interval and the received signal is corrupted by additive white Gaussian noise (AWGN). Our new expressions prove that the achievable performance holds irrespective of the true time delay value. Moreover, they corroborate previous attempts to empirically compute the considered bounds thereby enabling their immediate evaluation.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".