Next-Generation O-Band Coherent Transmission for 1.6 Tbps 10 km Intra-Datacenter Interconnects
Bibliographic record
Abstract
With the exponential growth of internet traffic, driven by the increasing number of connected devices and data-intensive applications, there is an urgent need to address the surging demand for higher capacity in datacenter communications. This paper proposes using O-band single-carrier coherent transmission to achieve 1.6 Tbps for intra-datacenter reach (2-10 km), leveraging the advancements in next-generation DACs and the TFLN platform. To support our proposal, we assess experimentally the gain of employing 256 GSa/s interleaved DACs compared to the current state-of-the-art 128 GSa/s DACs. Additionally, we explore the feasibility of utilizing cost-effective DFB lasers in these short-reach systems. Using the 128 GSa/s DAC and DFB lasers, we transmit 120 Gbaud DP-64QAM over 10 km of SSMF under the 20% overhead SD-FEC threshold, featuring a net rate of 1.2 Tbps. Switching to 256 GSa/s DAC, we achieve net 1.6 Tbps transmission over 10 km with 167 Gbaud DP-64QAM below the 25% SD-FEC BER threshold. We observe that the power penalty of using DFB lasers compared to ECLs is less than 1 dB. Furthermore, this study includes a comprehensive analysis of the power consumption envelope for various candidate configurations targeting 1.6 Tbps operation. The comparison reveals the competitiveness of the O-band single-carrier coherent solution, attributed to its simpler architecture and the inherent features of the TFLN platform. The analysis highlights the potential of the proposed solution as a power-efficient and high-performance option for meeting the demanding requirements of 1.6 Tbps Ethernet.
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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 | 0.000 |
| 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 | 0.000 |
| 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".