关键词: 雾无线接入网（F-RAN）, 深度学习, 资源分配, 深度神经网络, 频谱效率, 经济频谱效率

Abstract:

Consider performance optimization in fog radio access network, a resource allocation framework based on deep neural network is proposed. The framework aims to maximize the Economical Spectral Efficiency（ESE） through resource allocation strategy. In this framework, the resource allocation strategy to realize real-time processing is learned by means of a DNN while conventional resource allocation schemes require a considerable number of computations, and the beamforming can be determined using far fewer computations by a DNN. Through simulations, compared with the convex optimization based scheme and supervised learning based CNN scheme, the maximum gain of SE and ESE can be achieved respectively 5% and 20% in this framework. Furthermore, the execution time of the proposed framework is close to that of CNN scheme, which is much ahead of the traditional algorithm.

Key words: Fog Radio Access Network（F-RAN）, deep learning, resource allocation, Deep Neural Network（DNN）, spectral efficiency, economical spectral efficiency