Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are revolutionizing network management systems, paving the way towards fully autonomous and self-optimizing communication systems. These models enable networks to address complex decision-making tasks across both short-term operational scenarios and long-term strategic planning. Through natural language understanding, LLMs can analyze customer inquiries, predict network congestion patterns, and automate troubleshooting processes, leading to more efficient customer support and network maintenance. GenAI can optimize content delivery by generating personalized recommendations, improving user engagement, and dynamically adjusting network resources based on real-time demands, ultimately enhancing overall performance and user experience in telecommunication services. In this paper, we discuss the pivotal role of GenAI in advancing network performance and achieving the ultimate objective of self-adaptive networks. Moreover, we present a use case that leverages the self-attention mechanism of transformers to perform long-term traffic prediction. Harnessing these cutting-edge technologies demonstrates the transformative power of LLM and GenAI in revolutionizing telecommunication networks, elevating resilience and adaptability to unprecedented levels.

翻译：生成式人工智能（GenAI）与大规模语言模型（LLM）正在彻底变革网络管理系统，为构建完全自主和自优化的通信系统铺平道路。这些模型使网络能够应对短期运行场景和长期战略规划中的复杂决策任务。通过自然语言理解，LLM可以分析客户查询、预测网络拥塞模式并自动化故障排除流程，从而实现更高效的客户支持与网络维护。GenAI可通过生成个性化推荐来优化内容分发，提升用户参与度，并基于实时需求动态调整网络资源，最终增强电信服务的整体性能与用户体验。本文探讨了GenAI在提升网络性能及实现自适应网络最终目标中的关键作用。此外，我们提出了一个利用Transformer自注意力机制进行长期流量预测的应用案例。运用这些前沿技术，彰显了LLM与GenAI在革新电信网络、将韧性与适应性提升至前所未有的水平方面所具有的变革性力量。