Recurrent neural networks achieving MLSE performance for optical channel equalization
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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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 | 0.001 |
| 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".