This paper explores the integration of active machine learning (ML) for 6G
networks, an area that remains under-explored yet holds potential. Unlike
passive ML systems, active ML can be made to interact with the network
environment. It actively selects informative and representative data points for
training, thereby reducing the volume of data needed while accelerating the
learning process. While active learning research mainly focuses on data
annotation, we call for a network-centric active learning framework that
considers both annotation (i.e., what is the label) and data acquisition (i.e.,
which and how many samples to collect). Moreover, we explore the synergy
between generative artificial intelligence (AI) and active learning to overcome
existing limitations in both active learning and generative AI. This paper also
features a case study on a mmWave throughput prediction problem to demonstrate
the practical benefits and improved performance of active learning for 6G
networks. Furthermore, we discuss how the implications of active learning
extend to numerous 6G network use cases. We highlight the potential of active
learning based 6G networks to enhance computational efficiency, data annotation
and acquisition efficiency, adaptability, and overall network intelligence. We
conclude with a discussion on challenges and future research directions for
active learning in 6G networks, including development of novel query
strategies, distributed learning integration, and inclusion of human- and
machine-in-the-loop learning.

探索了将主动机器学习与 6G 网络集成的研究，该领域尚未得到充分探索，但具有潜力。与被动机器学习系统不同，主动机器学习可以与网络环境进行交互，并通过选择信息丰富、具代表性的数据点进行训练，从而减少所需的数据量并加速学习过程。此外，还探讨了生成人工智能和主动学习之间的协同作用，以克服主动学习和生成人工智能方面的现有限制。通过一个毫米波传输速率预测问题的案例研究，演示了主动学习在 6G 网络中的实际好处和性能改进。此外，还讨论了主动学习对于众多 6G 网络用例的潜力，包括提高计算效率、数据标注和采集效率、适应性和整体网络智能性。最后，探讨了主动学习在 6G 网络中的挑战和未来研究方向，包括开发新的查询策略、分布式学习集成，以及人机协同学习的加入。