用可编程光处理器优化长距多模传输,降低信号处理负担。
Programmable Photonic Unitary Processor Enables Parametrized Differentiable Long-Haul Spatial Division Multiplexed Transmission
- 在中继节点部署可编程光酉处理器,直接优化传输信道。
- 实验实现1300公里三模光纤传输,模拟与实测高度一致。
- 适合高速光通信、集成光计算领域研究人员参考。
全球数据流量激增亟需可扩展且低功耗的光通信系统。采用多芯或少模光纤的空间分复用(SDM)是突破单模光纤容量瓶颈的可行方案。然而,长距离SDM传输受模态色散影响,导致数字信号处理(DSP)需承担巨大计算负荷。本文提出参数化SDM传输架构,在中间节点部署可编程光酉处理器,不依赖仅在接收端进行的传统数字均衡,而是通过可编程酉变换直接优化传输信道,显著降低后续数字后处理负担。我们提出基于梯度的优化算法,利用可微分的SDM传输模型求解最优酉变换。作为关键使能技术,首次实现电信级可编程光酉处理器,具备低损耗(2.1 dB 光纤到光纤)、宽频带(全C波段)、偏振无关及高保真度(C波段内R²>96%)特性。实验验证了三模光纤1300公里传输,仿真与实测结果高度吻合。优化后的光处理器有效抑制模态色散,大幅降低后处理复杂度。本成果建立了一种将光计算融入光层的可扩展框架,推动更高效、高容量光网络发展。
原文摘要 · Abstract (English)
The explosive growth of global data traffic demands scalable and energy-efficient optical communication systems. Spatial division multiplexing (SDM) using multicore or multimode fibers is a promising solution to overcome the capacity limit of single-mode fibers. However, long-haul SDM transmission faces significant challenges due to modal dispersion, which imposes heavy computational loads on digital signal processing (DSP) for signal equalization. Here, we propose parameterized SDM transmission, where programmable photonic unitary processors are installed at intermediate nodes. Instead of relying on conventional digital equalization only on the receiver side, our approach enables direct optimization of the SDM transmission channel itself by the programmable unitary processor, which reduces digital post-processing loads. We introduce a gradient-based optimization algorithm using a differentiable SDM transmission model to determine the optimal unitary transformation. As a key enabler, we first implemented telecom-grade programmable photonic unitary processor, achieving a low-loss (2.1 dB fiber-to-fiber), wideband (full C-band), polarization-independent, and high-fidelity (R2>96% across the C-band) operation. We experimentally demonstrate 1300-km transmission using a three-mode fiber, achieving strong agreement between simulation and experiment. The optimized photonic processor significantly reduces modal dispersion and post-processing complexity. Our results establish a scalable framework for integrating photonic computation into the optical layer, enabling more efficient, high-capacity optical networks.
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