3.5 Gb/s burst-mode clock phase aligner for gigabit passive optical networks
Bibliographic record
Abstract
Fiber to the Home (FTTH) has been proven to be an efficient medium for voice, audio, and video transfer. Passive Optical Networks (PONs) are being studied as an upgradeable and low-cost solution to the problem of limited bandwidth in local access networks in the medium of FTTH. In PONs, multiple users share the fiber infrastructure in a pointto- multipoint (P2MP) network topology. The P2MP nature of networks cause the data packets from each user to undergo different amplitude, phase, and frequency variations – resulting in burst-mode traffic at the receiving end of the network. This consequently creates new challenges for the design of optical receivers. We design and experimentally demonstrate a 5 Gb/s burst-mode clock phase aligner (BM-CPA) featuring automatic phase acquisition with forward-error correction using (64, 57) Hamming codes. This BM-CPA is implemented with commercially available evaluation boards and provides instantaneous (0-bit) phase acquisition with packet loss ratio < 10^6 and bit error rate < 10^10 for any phase step (±2pi rads) between consecutive packets. Implementation of a Reed-Solomon(255, 239) code is also investigated. Our design is based on an oversampling algorithm and can be operated in two configurations: BM-CPA with a SONET CDR and BM-CPA with a local oscillator. We conclude by discussing various possible extensions to the device, based on the promising results.
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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".