Scalable Learning Paradigms for Data-Driven Wireless Communication
This work tackles scalability issues for wireless communication systems, but it is incremental as it provides a discussion rather than new solutions.
The paper addresses the scalability challenge in data-driven wireless networks due to exploding data volumes and model complexity, proposing a systematic discussion on scalable architectures, computing frameworks, and learning algorithms.
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.