Noise Analysis and Design Considerations for Equalizer-Based Optical Receivers
Bibliographic record
Abstract
Optical receiver front ends that are intentionally designed to have a bandwidth low enough that significant inter-symbol interference (ISI) is introduced are becoming commonplace. Although the resultant ISI must be removed using an equalizer, the lower bandwidth allows for higher gain in the front-end's first stage, lower input-referred noise, and fewer gain stages. With fewer main-amplifier stages, power dissipation is reduced. The noise analysis of these front ends presents several challenges. This paper derives integrated input-referred noise for inverter-based shunt-feedback transimpedance amplifiers from first principles and highlights the importance of correctly estimating the gain and noise bandwidth of the receiver. The notion of the effective gain of the receiver is introduced, which is lower than the midband gain typically used in noise calculations. The analysis of the inverter-based TIA is used to discuss the important design tradeoffs depending on the type of equalizer used. Integrated input-referred noise is derived and compared for front ends using decision-feedback equalizers (DFEs), continuous-time linear equalizers, and feed-forward equalizers. The simulation results show that a DFE-based receiver achieves the lowest input-referred noise.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".