Fog radio access networks (F-RANs), which consist of a cloud and multiple
edge nodes (ENs) connected via fronthaul links, have been regarded as promising
network architectures. The F-RAN entails a joint optimization of cloud and edge
computing as well as fronthaul interactions, which is challenging for
traditional optimization techniques. This paper proposes a Cloud-Enabled
Cooperation-Inspired Learning (CECIL) framework, a structural deep learning
mechanism for handling a generic F-RAN optimization problem. The proposed
solution mimics cloud-aided cooperative optimization policies by including
centralized computing at the cloud, distributed decision at the ENs, and their
uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs)
are employed for characterizing computations of the cloud and ENs. The
forwardpass of the DNNs is carefully designed such that the impacts of the
practical fronthaul links, such as channel noise and signling overheads, can be
included in a training step. As a result, operations of the cloud and ENs can
be jointly trained in an end-to-end manner, whereas their real-time inferences
are carried out in a decentralized manner by means of the fronthaul
coordination. To facilitate fronthaul cooperation among multiple ENs, the
optimal fronthaul multiple access schemes are designed. Training algorithms
robust to practical fronthaul impairments are also presented. Numerical results
validate the effectiveness of the proposed approaches.

本文介绍了一种名为 CECIL 的云端协同学习算法，利用深度神经网络优化 Fog 无线接入网络（FRAN）中的云端和边缘计算，以及前传接口。通过将云和边缘计算纳入联合优化框架，以及使用中央计算和分布式决策，并使用上下行前传协调来实现端到端的训练和去中心化的实时推断。