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<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> level 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 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".