The future 6G network is envisioned to be AI-native, and as such, ML models
will be pervasive in support of optimizing performance, reducing energy
consumption, and in coping with increasing complexity and heterogeneity. A key
challenge is automating the process of finding optimal model architectures
satisfying stringent requirements stemming from varying tasks, dynamicity and
available resources in the infrastructure and deployment positions. In this
paper, we describe and review the state-of-the-art in Neural Architecture
Search and Transfer Learning and their applicability in networking. Further, we
identify open research challenges and set directions with a specific focus on
three main requirements with elements unique to the future network, namely
combining NAS and TL, multi-objective search, and tabular data. Finally, we
outline and discuss both near-term and long-term work ahead.

未来 6G 网络是 AI 原生的，并且机器学习模型将广泛应用于优化性能、降低能耗以及应对不断增加的复杂性和多样性。本文描述和回顾了神经架构搜索和迁移学习在网络领域的最新技术，并针对未来网络领域的三个主要需求，即融合 NAS 和 TL、多目标搜索以及表格数据，识别了开放性的研究挑战和发展方向。最后，我们概述和讨论了近期和长期的工作展望。