Blind Estimation of Common Phase Error in OFDM and OFDMA
Bibliographic record
Abstract
This paper addresses the issue of blind estimation of common phase error (CPE) in OFDM systems affected by phase noise (PHN). Common approaches to blind CPE detection detect the symbols, and estimate the phase noise in an iterative manner. An important assumption that these decision-directed algorithms make is that a majority of the symbols detected in the first iteration, while ignoring the presence of phase noise, have been detected correctly. This assumption fails to hold under scenarios of high CPE and leads to a premature error floor. In this paper we dispense with the assumption that most of the symbols have been detected correctly and instead associate with each symbol a certain probability of having been detected correctly. Through the introduction of an auxiliary binary variable that indicates whether the right decision on a symbol has been made or not, we design a new algorithm to estimate CPE. This algorithm is robust to high CPE scenarios and is able to lower the error floor seen at high SNRs.
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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 | 0.000 | 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".