Next-Generation O-Band Coherent Transmission for 1.6 Tbps 10 km Intra-Datacenter Interconnects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it