It is widely perceived that leveraging the success of modern machine learning techniques to mobile devices and wireless networks has the potential of enabling important new services. This, however, poses significant challenges, essentially due to that both data and processing power are highly distributed in a wireless network. In this paper, we develop a learning algorithm and an architecture that make use of multiple data streams and processing units, not only during the training phase but also during the inference phase. In particular, the analysis reveals how inference propagates and fuses across a network. We study the design criterion of our proposed method and its bandwidth requirements. Also, we discuss implementation aspects using neural networks in typical wireless radio access; and provide experiments that illustrate benefits over state-of-the-art techniques.

翻译：人们普遍认为，将现代机器学习技术的成功应用于移动设备和无线网络，有望赋能重要的新型服务。然而，这带来了重大挑战，根本原因在于无线网络中的数据和处理能力高度分布。本文提出了一种学习算法与架构，不仅在训练阶段，而且也在推理阶段利用了多个数据流和处理单元。特别地，分析揭示了推理如何在网络中传播与融合。我们研究了所提方法的设计准则及其带宽需求。此外，还讨论了在典型无线接入网中使用神经网络实现的若干方面，并提供了实验，展示了相较于现有技术的优越性。