MMSE Equalizer Design Optimization for Wireline SerDes Applications
Bibliographic record
Abstract
This paper presents analytical equations for optimizing feedforward equalizer (FFE) and decision feedback equalizer (DFE) parameters in a wireline receiver to speed up system-level design and simulations. A minimum mean square error (MMSE)-based approach is applied to the receiver model, and a set of equations is developed to co-optimize FFE and DFE taps. The equations consider the noise sources in wireline links, including the sampling clock jitter. It also considers the effect of the noise correlations on the equalizer parameters. For sampling clock jitter, two separate models are developed to distinguish between sampling for discrete-time and continuous-time FFEs (pre-and post-FFE sampling). Then, the translation of jitter noise to voltage noise is carefully investigated. Jitter noise can be either white or correlated. Later, the developed model is modified to generate different variants of MMSE-based approaches to be used in various practical scenarios a designer may face. This includes the equalizer design for maximum likelihood sequence estimation (MLSE)-based receivers and equalizer design with bounded DFE tap magnitude to control undesired side effects such as error propagation. Finally, the use of “tap skipping” to save FFE hardware resources is investigated. The accuracy of models and the performance of each method is justified through simulations and comparing against the LMS adaptation loops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".