FBMC vs. PAM and DMT for High-Speed Wireline Communication
Bibliographic record
Abstract
This paper demonstrates the first silicon-verified FBMC encoder and decoder designed to emulate beyond$224Gb/s$wireline communication. It also compares the performance of FBMC to PAM and DMT in three steps. First, the digital power and area consumption are compared using measured results from the manufactured test chip. Second, the data rate is determined using lab-measured results. And third, the performance when subject to notched channels is analyzed using simulation results. Finally, we present a method to emulate wireline links while reducing the emulator complexity and simulation time by one to two orders of magnitude over conventional over-sampled techniques. Our analysis indicates that given a smooth channel and an SNR which enables an average spectral efficiency of$4bits/sec/Hz$at a bit-error rate of 10-3, both DMT and FBMC perform similarly to a conventional PAM-4 link. However, when noise is reduced and a spectral notch is applied, thereby achieving an average spectral efficiency of$4.6bits/sec/Hz$, DMT and FBMC can outperform PAM by 2.1 and 2.3 times, respectively. In addition, we estimate FBMC’s encoder and decoder power consumption at$1.53pJ/b$and$1.98pJ/b$, respectively, and area requirement at$0.07mm^{2}$and$0.17mm^{2}$, respectively, which is similar to DMT. These values are competitive with similar$22nm$PAM transceivers, suggesting that DMT and FBMC are viable alternatives to PAM for next-generation high-speed wireline applications.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".