Near-Optimal Training-Based Estimation of Frequency Offset and Channel Response in OFDM with Phase Noise
Bibliographic record
Abstract
We propose an efficient training-based OFDM channel impulse response (CIR) and carrier frequency offset (CFO) estimation algorithm that addresses the problem of phase noise (PHN), assuming that the PHN has a known prior distribution. The optimal joint estimation of CIR, PHN and CFO was described in an earlier work of ours. In this paper, we focus on the case where a training symbol consists of two identical halves in the time domain, and propose a variant to Moose's CFO estimation algorithm that accounts for PHN in CFO estimation. This is followed by an optimal joint CIR and PHN estimation scheme tailored for this "repeating training symbol" setup. It is assumed that the PHN process is Gaussian with known mean and covariance matrix. This encompasses both Wiener PHN and Gaussian PHN. It is shown through simulations that the proposed algorithm performs almost as well as the optimal JCPCE algorithm at much lower complexity. To further reduce the complexity of the proposed scheme, the conjugate gradient (CG) method is used and we show that it can be realized using the Fast Fourier Transform (FFT).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".