Bibliographic record
Abstract
This thesis explores advanced equalization methods and noise cancellation techniques in high-speed wireline communication systems. The contributions of this research are as follows: The first part of this thesis investigates maximum likelihood sequence estimation (MLSE) for wireline applications. While MLSE is an optimal algorithm for data recovery, its high area and power consumption often restrict its use in wireline applications. This research explores and implements an on-demand MLSE method that leverages errors propagated by the decision-feedback equalizer (DFE). In this approach, the DFE output is used as the receiver's output unless an error is detected, at which point the MLSE is activated to provide a more accurate result. This on-demand strategy reduces both power and area requirements. To validate this method, we designed an MLSE-on-demand for a 4-PAM, 1+D signaling system and synthesized it using 16nm FinFET TSMC technology. For comparison, a conventional MLSE was also synthesized using the same technology. The synthesis report confirms that the proposed design consumes only 1/10 of the power and occupies 1/15 of the area required by the conventional MLSE, while maintaining a comparable symbol error rate (SER). The second part investigates the techniques for reducing the noise enhanced by the linear equalizers in high-speed wireline communication. By comparing feedforward and feedback architectures, it is found that while feedforward noise cancelling filters (FF-NCF) can reduce total noise power, they often fail to mitigate the SER due to unreliable noise estimation. To address this challenge, an optimal noise estimation and cancellation filter (ONECF) is proposed, directly minimizing SER. Mathematical analysis and experimental results demonstrate ONECF's effectiveness in reducing SER and improving signal-to-noise ratio (SNR), with the degree of improvement proportional to the channel loss. In the final part, a method to enhance the SER performance of FF-NCF is presented. This approach dynamically switches the receiver's output between FF-NCF and feedforward equalizer (FFE) based on the reliability of estimated noise values within FF-NCF, effectively mitigating error propagation and preventing receiver performance degradation. Simulations show SER improvement compared to conventional FF-NCF structures, approaching the performance of feedback noise cancelling filters (FB-NCF) with lower complexity. Furthermore, this approach reduces the likelihood of long burst errors, particularly advantageous for wireline receivers employing forward error correction (FEC).
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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) | 0.003 | 0.001 |
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".