The development of large language models (LLM) has revolutionized various fields and is anticipated to drive the advancement of autonomous systems. In the context of autonomous optical networks, creating a high-level cognitive agent in the control layer remains a challenge. However, LLM is primarily developed for natural language processing tasks, rendering them less effective in predicting the physical dynamics of optical communications. Moreover, optical networks demand rigorous stability, where direct deployment of strategies generated from LLM poses safety concerns. In this paper, a digital twin (DT)-enhanced LLM scheme is proposed to facilitate autonomous optical networks. By leveraging monitoring data and advanced models, the DT of optical networks can accurately characterize their physical dynamics, furnishing LLMs with dynamic-updated information for reliable decision-making. Prior to deployment, the generated strategies from LLM can be pre-verified in the DT platform, which also provides feedback to the LLM for further refinement of strategies. The synergistic interplay between DT and LLM for autonomous optical networks is demonstrated through three scenarios: performance optimization under dynamic loadings in an experimental C+L-band long-haul transmission link, protection switching for device upgrading in a field-deployed six-node mesh network, and performance recovery after fiber cuts in a field-deployed C+L-band transmission link.

翻译：大型语言模型（LLM）的发展已革新多个领域，并有望推动自主系统的进步。在自主光网络的背景下，在控制层创建高级认知代理仍是一项挑战。然而，LLM主要针对自然语言处理任务开发，使其在预测光通信物理动态方面效果有限。此外，光网络要求严格的稳定性，直接部署LLM生成的策略会引发安全隐患。本文提出一种数字孪生（DT）增强的LLM方案，以促进自主光网络的发展。通过利用监测数据和先进模型，光网络的数字孪生能够精确表征其物理动态，为LLM提供动态更新的信息以支持可靠决策。在部署前，LLM生成的策略可在DT平台中进行预验证，该平台同时向LLM提供反馈以进一步优化策略。通过三个场景展示了DT与LLM在自主光网络中的协同交互：实验性C+L波段长距离传输链路在动态负载下的性能优化、现场部署的六节点网状网络中设备升级的保护切换，以及现场部署的C+L波段传输链路在光纤切断后的性能恢复。