2020/03/01 by Yue Xu, Feng Yin, Xu, Yue +9 · 1 citation
Computer Science · Engineering · #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Networking and Internet Architecture (cs.NI) #Wireless Body Area Networks
paper · pdf · doi:10.48550/arxiv.2003.00474
openalex publication_date 2020/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.