Scalable Learning Paradigms for Data-Driven Wireless Communication

doi:10.1109/MCOM.001.2000143

Scalable Learning Paradigms for Data-Driven Wireless Communication

2020-10-01

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DOI：10.1109/MCOM.001.2000143

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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.

Scalable Learning Paradigms for Data-Driven Wireless Communication

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