AI 中文总结
针对AI数据中心网络的短距光互连速率提升需求，提出包含多类方法的统一优化框架，经仿真实验验证决策树、Transformer等方法可显著提升40 Gb/s 10 km链路性能。
AI 中文摘要
受AI驱动的数据中心网络的带宽和能效需求推动，短距光互连正朝着每通道超过400 Gb/s的速率发展。在这些工作速率下，信道损伤、器件非线性和硬件约束限制了传统收发器设计和数字信号处理（DSP）的有效性。本文提出一种用于优化直接检测光互连的统一框架，涵盖数字代理建模、接收端DSP优化以及端到端（E2E）收发器学习。所提出的公式为基于模型和基于机器学习的方法提供了共同视角，包括线性和非线性均衡、查找表、决策树、神经网络接收器以及E2E优化。结合计算复杂度和硬件实现方面讨论了它们的性能，突出了相关权衡。我们通过仿真和实验验证表明，对于40 Gb/s、10 km的链路，决策树相比传统线性均衡可实现0.5~1 dB的增益，且硬件需求可忽略；此外，基于Transformer的E2E技术可提供高达6 dB的增益，凸显了联合收发器优化对提升下一代短距光链路性能的潜力。
英文摘要
Short-reach optical interconnects are evolving toward data rates beyond 400 Gb/s per lane, driven by the bandwidth and energy-efficiency requirements of AI-enabled datacenter networks. At these operating speeds, channel impairments, device nonlinearities, and hardware constraints limit the effectiveness of conventional transceiver design and digital signal processing (DSP). This paper presents a unified framework for the optimization of direct-detection optical interconnects, encompassing digital surrogate modeling, receiver-side DSP optimization, and end-to-end (E2E) transceiver learning. The proposed formulation provides a common perspective for model-based and machine-learning-based approaches, including linear and nonlinear equalization, lookup tables, decision trees, neural-network receivers, and E2E optimization. Their performance is discussed together with computational complexity and hardware implementation aspects, highlighting the associated trade-offs. We show, through simulations and experimental validations, that for a 40~Gb/s 10~km link, decision trees can outperform by 0.5--1~dB conventional linear equalization, with negligible hardware requirements. Moreover, we show that an E2E technique based on transformers can provide a gain up to 6~dB, highlighting the potential of joint transceiver optimization to improve the performance of next-generation short-reach optical links.
