The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM
Bibliographic record
Abstract
This paper studies the mitigation of phase noise (PHN) in orthogonal frequency-division multiplexing (OFDM) data detection. We present a systematic probabilistic framework that leads to both optimal and near-optimal OFDM detection schemes in the presence of unknown PHN. In contrast to the conventional approach that cancels the common (average) PHN, our aim is to jointly estimate the complete PHN sequence and the data symbol sequence. We derive a family of low-complexity OFDM detectors for this purpose. The theoretical foundation on which these detectors are based is called variational inference, an approximate probabilistic inference technique associated with the minimization of variational free energy. In deriving the proposed schemes, we also point out that the expectation-maximization algorithm is a special case of the variational-inference-based joint estimator. Further complexity reduction is obtained using the conjugate gradient (CG) method, and only a few CG iterations are needed to closely approach the ideal joint estimator output
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".