Recurrent neural networks achieving MLSE performance for optical channel equalization
Why this work is in the frame
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Bibliographic record
Abstract
We explore recurrent and feedforward neural networks to mitigate severe inter-symbol interference (ISI) caused by bandlimited channels, such as high speed optical communications systems pushing the frequency response of transmitter components. We propose a novel deep bidirectional long short-term memory (BiLSTM) architecture that strongly emphasizes dependencies in data sequences. For the first time, we demonstrate via simulation that for QPSK transmission the deep BiLSTM achieves the optimal bit error rate performance of a maximum likelihood sequence estimator (MLSE) with perfect channel knowledge. We assess performance for a variety of channels exhibiting ISI, including an optical channel at 100 Gbaud operation using a 35 GHz silicon photonic (SiP) modulator. We show how the neural network performance deteriorates with increasing modulation order and ISI severity. While no longer achieving MLSE performance, the deep BiLSTM greatly outperforms linear equalization in these cases. More importantly, the neural network requires no channel state information, while its performance is comparable to conventional equalizers with perfect channel knowledge.
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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 | 0.000 | 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 it