Detection of High Baud-Rate Signals With Pattern Dependent Distortion Using Hidden Markov Modeling
Bibliographic record
Abstract
In high baud-rate systems, the bandwidth limitations and nonlinearities of drive amplifiers and optical modulators can introduce pattern dependent distortion (PDD) that limits system performance. One solution entails detecting the transmitted symbols with the aid of a look-up table (LUT) containing prototypes of the PDD degraded signal. To improve the performance-complexity trade-offs of this approach, we model the PDD degraded signal as drawn from a hidden Markov model (HMM). Detection of the transmitted symbols given the received signal is performed by finding the HMM state sequence that emits the received signal with maximum a posteriori probability (MAP). Computational complexity is kept manageable by using the Viterbi algorithm to find the MAP state sequence, and by simplifying the HMM emission probability functions to produce variants of the algorithm. The resultant set of algorithm variants subsume a few recent LUT-based nonsequential detection schemes. In a back-to-back experiment, the proposed solutions demonstrate 6-fold lower computational complexity, compared to their nonsequential counterparts for the same target bit error ratio. In a 3 × 402 Gb/s dual-polarization 16-QAM superchannel transmission experiment, the sequential approach offers a 37% reach extension over a nonsequential LUTbased benchmark algorithm. Overall, HMM-based sequential detection offers superior performance-complexity trade-offs over the LUT-based nonsequential detection algorithms.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".