  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.
