Advanced technologies for next generation optical transceivers in long haul transport links and short reach interconnects
Bibliographic record
Abstract
The burgeoning capacity demand for both optical transport links over metro / long-haul distances (> 100km) and short reach optical interconnects (100m-20km) for inter-and intra-datacenter networking is driving the notion of a digital signal processing (DSP) based optical transceiver.In optical transport, flexible optical transceivers employ coherent detection and DSP to enable mitigating various fiber transmission impairments such as chromatic dispersion (CD), polarization mode dispersion (PMD), Kerr nonlinearity, laser phase noise and frequency offset.In addition, these flexible transceivers need to be agile enough to adapt to the increasingly dynamic network traffic needs (e.g.varying capacity and reach requirements, dynamic wavelength assignments).On the short reach side, cheap and power efficient optics is a necessity for datacenter applications and hence intensity modulation / direct-detection (IM/DD) prevails over coherent detection.For such IM/DD transceivers, photonic integration is envisioned to play a significant role in reducing the cost along with DSP that potentially allows increasing the delivered bit rates with cheap electronics and reduced number of optical components (and consequently footprint).In the first part of the thesis, we propose and verify, both analytically and experimentally, a myriad of DSP algorithms suitable for implementation in flexible optical coherent transceivers for transport networks.The proposed DSP algorithms tackle impairments such as laser phase noise, frequency offset, polarization rotation, fiber Kerr nonlinearity and sampling frequency offset.For each of the proposed algorithms, we provide a performance comparison with the standard counterparts in the literature highlighting the advantages of our approaches together with the underlying tradeoffs.Next, we develop and experimentally verify a rigorously derived analytical model for performance evaluation of a flexible coherent optical front-end that can operate colorlessly in a wavelength division multiplexing (WDM) scenario, i.e. without the need for a WDM demultiplexing filter prior to the receiver.The accuracy of the developed model was proved by finding a close match between model predictions and experimental results.We finally show that the model can be used as a useful system design tool to predict the system performance at various operating scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".