论文标题
用于数据驱动的无线通信的可扩展学习范例
Scalable Learning Paradigms for Data-Driven Wireless Communication
论文作者
论文摘要
无线大数据和机器学习技术的婚姻通过数据驱动的理念彻底改变了无线系统。但是，不断爆炸的数据量和模型复杂性将限制集中的解决方案以在合理的时间内学习和响应。因此，可伸缩性成为要解决的关键问题。在本文中，我们旨在就可扩展数据驱动的无线网络的构建块进行系统的讨论。一方面，我们从全球角度讨论了可扩展数据驱动系统的前瞻性体系结构和计算框架。另一方面，我们从当地的角度讨论了在每个单个节点上执行的学习算法和模型培训策略。我们还在可扩展数据驱动的无线通信的背景下强调了几个有前途的研究方向，以激发未来的研究。
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy. However, the ever exploding data volume and model complexity will limit centralized solutions to learn and respond within a reasonable time. Therefore, scalability becomes a critical issue to be solved. In this article, we aim to provide a systematic discussion on the building blocks of scalable data-driven wireless networks. On one hand, we discuss the forward-looking architecture and computing framework of scalable data-driven systems from a global perspective. On the other hand, we discuss the learning algorithms and model training strategies performed at each individual node from a local perspective. We also highlight several promising research directions in the context of scalable data-driven wireless communications to inspire future research.
