2D Constellation Distortion for Subduing Equalization Noise in Bandwidth- Limited IMDD Systems
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/$\lambda $PAM4 and PAM6 in the O-band over 2 km of SSMF at a BER of$3.8\times 10^{-3}$below the 6.7% overhead HD-FEC using a 47 GHz SiP modulator, linear equalization, and the proposed 2D distortion.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".