Bibliographic record
Abstract
A central challenge in the design of electrical links is to compensate for frequency-dependent loss in the channel that introduces inter-symbol interference (ISI). This chapter presents the overall objectives of joint Tx/Rx equalization. The system-level operation of transmitter-side feed-forward equalizers (FFEs) is discussed. Circuit details are presented in Chapter 5. Receiver-side continuous-time linear equalizers (CTLEs) and finite-impulse-response (FIR) filters are discussed next, followed by decision-feedback equalizers (DFEs). DFEs differ from FFEs, CTLEs and FIRs in that they only remove ISI rather than attempt to invert the low-pass channel characteristic. With the growing trend toward ADC- based receivers, the implementation of DFEs and Rx FFEs is discussed in the analog domain and the digital domain. The topics in this chapter are also a relevant background for the sections in Chapter 10 that discuss TIAs for reduced bandwidth systems, where equalization is used to remove ISI from an intentionally bandwidth-limited optical receiver front-end.
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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".