Bibliographic record
Abstract
The research presented in this thesis involves implementing a high data rate wireline communication system using Discrete Multitone (DMT) transmission.A theoretical analysis of the model of channels typically used for serializer/deserializer SERDES chip-to-chip communication is done, showing the benefits of DMT through improved spectral efficiency, and simplified transceiver design due to the pseudo-narrowband characteristics of DMT.Simulations results demonstrate this benefit by being able to achieve higher data rates than conventionally used non-return-to-zero (NRZ) and pulse-amplitude modulation (PAM) typically used, even with typical channel correction circuitry such as continuous-time linear equalizers (fs) and decision-feedback equalizers (DFEs).Furthermore, a combined bit-loading/power allocation and transmit side equalization algorithm is presented that can improve the data rate of the system and decrease its bit error rate.Measurement results are demonstrated using a digital-to-analog-converter (DAC) and analog-to-digital converter (ADC) test bed in realistic conditions for chip-to-chip communications with a data rate over 250 GB/s with a sufficient overhead for forward-error-correction (FEC) coding needed to reduce the bit-error rate (BER).
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".