Pre-Emphasis for 1.2 Tb/s DP-64QAM Transmission Simulated in OptiSystem
Bibliographic record
Abstract
We investigate analog and digital pre-emphasis for ultra-high-bit-rate coherent dual-polarization 64-QAM (DP-64QAM) transmission using OptiSystem. Two representative single-wavelength configurations are studied: 64 Gbaud (600 Gb/s payload, 768 Gb/s line rate) and 100 Gbaud (1000 Gb/s payload, 1.2 Tb/s line rate). The transmitter employs raised-cosine pulse shaping (roll-off 0.1) and a 9-bit DAC, while the receiver uses a 9-bit ADC; bandwidth-limiting Bessel/Gaussian filters emulate practical transmitter (Tx) and receiver (Rx) front-end constraints. Analog pre-emphasis (APE) is realized by uploading a measured analog filter response immediately after the DAC to compensate high-frequency roll-off. Digital pre-emphasis (DPE) is implemented before the DAC as a finite-impulse-response (FIR) pre-distortion stage, with taps obtained from the measured frequency response via spectrum mirroring, inverse FFT, Hamming-window smoothing, and normalization. We compare four cases: (i) ideal reference without bandwidth limits; (ii) bandwidth-limited without pre-emphasis; (iii) APE; and (iv) DPE. Bit-error-rate–versus–optical signal-to-noise ratio (OSNR) results show that both APE and DPE substantially mitigate bandwidth-induced penalties and approach the theoretical bound, reducing the OSNR gap to 5.8 dB at 64 Gbaud and 6.6 dB at 100 Gbaud, with operation near the forward error correction (FEC) threshold (BER=10−2). While DPE offers full programmability, it increases peak-to-average power ratio (PAPR) and may require additional gain headroom. Overall, APE provides an effective rapid-prototyping step prior to DPE deployment, confirming the feasibility of 768 Gb/s and 1.2 Tb/s DP-64QAM links with commercially realistic components, including a 150 GSa/s DAC operating at 1.5 samples/symbol for 100 Gbaud.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".