2D Constellation Distortion for Subduing Equalization Noise in Bandwidth- Limited IMDD Systems
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Bibliographic record
Abstract
Bandwidth-limited IMDD systems suffer from the noise boosted by the strong equalization at the receiver. This letter proposes a multiplication-free approach that reduces the impacts of the equalizer-enhanced colored noise through time-interleaving the received symbols and distorting the two-dimensional (2D) constellation such that the noise correlation is subdued. The 2D distortion map is predefined and retrieved from a look-up table. The proposed 2D constellation distortion is evaluated after the linear feed-forward equalizer and Volterra nonlinear equalizer. Experimental results show a BER reduction of 40% when the 2D constellation distortion is employed after the equalizer for the 135 Gbaud PAM4 and 110 Gbaud PAM6 signals. Owing to the proposed approach, we transmit a net data rate of 250 Gbps/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> PAM4 and PAM6 in the O-band over 2 km of SSMF at a BER of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.8\times 10^{-3}$ </tex-math></inline-formula> below the 6.7% overhead HD-FEC using a 47 GHz SiP modulator, linear equalization, and the proposed 2D distortion.
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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 | 0.001 | 0.001 |
| 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 it