Robust Wireless Fronthauling Methods for Decentralized Deep Learning in Fog-RAN

This paper designs wireless fronthauling techniques for deep learning (DL) enabled fog radio access networks (F-RANs) where computation and communication processes at a cloud and edge nodes (ENs) are carried out by deep neural networks (DNNs). Coordination among ENs and the cloud is realized by wireless fronthaul links, which incurs undesired randomness in forwardpass calculations of DNNs. To address this issue, we propose a robust training strategy whereby a group of DNNs can mitigate the impairment from fronthaul fading and additive noise. Numerical results demonstrate the superiority of the proposed robust wireless fronthauling scheme.