论文标题
6G的集成感应和通信：十个钥匙机学习角色
Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles
论文作者
论文摘要
集成传感和通信是未来无线系统的定义主题。这是由有希望的性能提高的动机，尤其是当它们互相协助时，以及更好地利用无线和硬件资源。但是，在实践中意识到这些收益会遇到一些挑战，在这些挑战中，机器学习可以提供潜在的解决方案。本文重点介绍了十个关键的机器学习角色，用于连接感应和交流，感应辅助的通信以及通信辅助感应系统，解释了为什么可以利用机器学习以及如何使用机器学习，并突出了未来研究的重要方向。本文还根据大规模的现实数据集DeepSense 6G提供了这些机器学习角色中的一些现实结果，这些结果可以在研究广泛的集成感应和通信问题时采用。
Integrating sensing and communication is a defining theme for future wireless systems. This is motivated by the promising performance gains, especially as they assist each other, and by the better utilization of the wireless and hardware resources. Realizing these gains in practice, however, is subject to several challenges where leveraging machine learning can provide a potential solution. This article focuses on ten key machine learning roles for joint sensing and communication, sensing-aided communication, and communication-aided sensing systems, explains why and how machine learning can be utilized, and highlights important directions for future research. The article also presents real-world results for some of these machine learning roles based on the large-scale real-world dataset DeepSense 6G, which could be adopted in investigating a wide range of integrated sensing and communication problems.
