Analytical and experimental performance evaluation of an integrated Si-photonic balanced coherent receiver in a colorless scenario
Bibliographic record
Abstract
We study analytically and experimentally the performance limits of a Si-photonic (SiP) balanced coherent receiver (CRx) co-packaged with transimpedance amplifiers (TIAs) in a colorless WDM scheme. Firstly, the CRx architecture is depicted and characterization results are presented. Secondly, an analytical expression for the signal-to-noise ratio (SNR) at the CRx output is rigorously developed and various noise sources in the context of colorless reception are outlined. Thirdly, we study experimentally the system-level CRx performance in colorless reception of 16 × 112 Gbps PDM-QPSK WDM channels. Using a 15.5 dBm local oscillator (LO) power, error free transmissions over 4800 and 4160 km at received powers of -3 and -21 dBm per channel, respectively, were achieved in a fully colorless and preamplifierless reception. Next, a set of measurements on one of the center WDM channels is performed where the LO power, received signal power, distance, and number of channels presented to the CRx are swept to evaluate the performance limits of colorless reception. Results reveal that the LO beating with optical noise incoming with the signal is a dominant noise source regardless of received signal power. In the high received signal power regime (~0 dBm/channel), the self-beat noise from out-of-band (OOB) channels is an additional major noise source especially for small LO-to-signal power ratio, short reach and large number of OOB channels. For example, at a received signal power of 0 dBm/channel after 1600 km transmission, the SNR difference between the fully filtered and colorless scenarios, where 1 and 16 channels are passed to the CRx respectively, grows from 0.5 to 3.3 dB as the LO power changes from 12 to 0 dBm. For low received power (~-12 dBm/channel), the effect of OOB channels becomes minor while the receiver shot and thermal noises become more significant. We identify the common mode rejection ratio (CMRR) and sensitivity as the two important CRx specifications that impact the performance at high and low received signal power regimes, respectively. Finally, an excellent match between experimental and analytical SNRs is proven after the derived SNR model is fitted to the experimental data in a least-squares sense. The model is then used to predict that the CRx can operate colorlessly for a fully populated WDM spectrum with 80 channels provided that the LO-to-signal power ratio is properly set.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".