Discrete Multitone Modulation for VSR and MR Electrical Interconnects and Optical Links
Bibliographic record
Abstract
A rich modulation scheme based transceiver is designed and implemented with throughput and error rate being given a high priority in order to target the continued exponential growth in the demand of data traffic ranging from intrachip high speed busses to metropolitan scale data center interconnects.A thorough analysis of the selection of key design details is performed both through analytical derivations and measurements that have not been previously done.Measurements using existing repurposed hardware are obtained in both an electrical very short to medium reach interconnect and an optical loop-back channel both containing a channel loss of 18dB.In conjunction to that, this work merges optimization techniques and introduces some that has not been used in previous work.This resulted in a transceiver achieving 250 Gbps, an average of 3.5 × 10 -4 BER, a spectral efficiency of 5.8 b/s/Hz, and an estimated power consumption of 5.1 pJ/b, qualifying it be superior to prior art as well as suitable for both low power and high throughput applications in both coherent and non-coherent flavours of transceivers.It also serves as a blueprint for applying and adapting these optimization methods to varying design architectures, limitations of the analog circuitry, and frequency response of the system.A great deal of appreciation goes to Dr. Calvin Plett for his supervision, support, deep technical and theoretical expertise, and efforts throughout this project.From accepting to taking me under his wing to his heavy involvement in a the project, only go to show the care he has for his students and genuine curiosity and enthusiasm of navigating technical challenges and discussing theory.Equally as such, a thank you to Dr. Naim Ben-Hamida who is always prepared to provide his guidance and input on the project ranging from high-level concepts to intricate details.His breadth and depth of knowledge is likely unsurpassed in the industry.A thank you to Ciena Corporation for the opportunity to perform this project with access to state of the art equipment and exposure to brilliant individuals.Their dedication to the field of research is priceless to the development of young and upcoming engineers and to the telecommunications industry as a whole.
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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 | 0.000 |
| 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 | 0.000 |
| 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".