Deep Learning-Assisted Phase Noise Mitigation for High-Order Modulation with Minimal Overhead
Bibliographic record
Abstract
This paper introduces Deep Learning Assisted phase noise Estimation (DLAE), a novel method for mitigating phase noise (PN) in high-order modulations in wireless backhaul links. DLAE leverages the inherent inter-correlation among signals affected by PN, employing a tailored Convolutional Neural Network (CNN) to overcome limitations observed in the existing Pilot-Symbol Assisted Modulation (PSAM) scheme. As a non-iterative solution, DLAE consistently upholds its excellent estimation accuracy, even with increased pilot spacing, rendering it particularly suitable for throughput-centric scenarios like backhaul applications. In terms of bit error rate, DLAE outperforms the PSAM scheme by over 2 dB at the 10−6level across 256-QAM, 1024-QAM, and 4096-QAM coded modulation systems under strong PN. Noteworthy is DLAE's achievement of this performance with a reduction in pilot overhead. The computational complexity of DLAE, linked to the CNN size, remains independent of the constellation size, reinforcing its practicality in high-order coded modulation systems.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".