Low Overhead Intra-Symbol Carrier Phase Recovery for Reduced-Guard-Interval CO-OFDM
Bibliographic record
Abstract
We propose intra-symbol carrier phase recovery (IS-CPR) for reduced-guard-interval (RGI) CO-OFDM in order to compensate for the intra-symbol phase shift (ISPS) between subcarriers that is caused by the dispersion-enhanced phase noise (DEPN). We begin by proposing a pre-emphasized pilot subcarrier (PEPS) approach to reduce the pilot subcarrier overhead for the following IS-CPR algorithms. Then, we show a statistical analysis of the DEPN-induced ISPS between subcarriers within one symbol, which is related to the accumulated chromatic dispersion (CD). Next, three algorithms are proposed for IS-CPR including maximum-likelihood (ML) phase estimation, digital phase-locked loop (DPLL), and feedforward carrier recovery (FFCR) employing either theMth power scheme in case of QPSK modulation or the QPSK partitioning scheme for the 16-QAM case. The performance and complexity of these algorithms are compared. Through simulations, we show that in comparison to conventional common phase error (CPE) compensation, IS-CPR significantly improves the linewidth tolerance at 1 dB signal-to-noise ratio (SNR) penalty for a bit error rate (BER) = 10-3from 300 kHz to 2 MHz for 112 Gb/s systems (28 Gbaud QPSK) at 3200 km transmission distance, and from 70 kHz to 550 kHz for 448 Gb/s (56 Gbaud 16-QAM) systems at 1600 km transmission distance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".