Scalable Learning Paradigms for Data-Driven Wireless Communication

• URL: http://arxiv.org/abs/2003.00474v1
• Date: Sun, 1 Mar 2020 12:13:58 GMT
• Title: Scalable Learning Paradigms for Data-Driven Wireless Communication
• Authors: Yue Xu, Feng Yin, Wenjun Xu, Chia-Han Lee, Jiaru Lin, Shuguang Cui
• Abstract summary: 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.
• Score: 45.03425546213185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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