Inverse design of discrete Raman amplifiers using an invertible neural network for ultra-wideband optical transmission based on hollow core fibers
Zheyu Wu, Ran Gao, Fei Wang, Huan Chang, Zhipei Li, Dong Guo, Lei Zhu, Qi Zhang, Guangquan Wang, Shikui Shen, Yanbiao Chang, Xiangjun Xin
Abstract
The increasing demand for higher data transmission rates in optical networks has led to significant advancements in hollow core fibers (HCFs), particularly in multi-band transmission (MBT) systems. These systems, which use the previously untapped spectrum, require efficient amplification solutions to achieve stable long-distance transmission. Raman amplifiers, and especially discrete Raman amplifiers (DRAs), have emerged as promising candidates due to their broad bandwidth and tunable gain. However, optimizing the complex multi-pump configurations in DRAs remains a long-standing challenge. In this study, we propose an inverse design method based on an invertible neural network (INN) to efficiently determine DRA pump configurations for specific gain profiles. By combining a global search via the INN and local refinement through a fully connected neural network, our method achieves precise control over the gain flatness and pump parameters. Experimental validation over a 10 km spectral transmission system shows an 18 dB gain level with a gain flatness of 2 dB, confirming the effectiveness of the proposed inverse design method. In addition, the feasibility of the proposed design in practical applications was confirmed in the 25GBaud 64-QAM data transmission experiment based on HCF. This approach offers what we believe to be new opportunities for the optimisation of wideband optical networks.