Scalable Learning Paradigms for Data-Driven Wireless Communication
Yue Xu, Feng Yin, Wenjun Xu, Chia‐Han Lee, Jiaru Lin, Shuguang Cui
Abstract
The marriage of wireless big data and machine learning techniques revolutionizes wireless systems by introducing 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 of 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 relevant 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.