An artificial intelligence-based networking method for fog radio access networks, which includes: a central computing logic module receives reported data which includes measurement report data from user terminals, wireless transmission data from base stations, and operation and maintenance data from a radio access network. Based on these reported data and proper machine learning algorithms, the central computing logic module configures an operating mode of a radio access network that matches user behavior, service attributes, and radio access network performance indicators. According to the operating mode, an edge computing logic module determines whether to optimize a current configuration of an edge communication entity and allocation of radio resources, computing resources, and caching resources. With proper machine learning algorithms, the proposed networking method meets various service requirements. By configuring the radio access network flexibly, the method enables the radio access network to adapt to different application scenarios and performance objectives.
Artificial intelligence-based networking method and device for fog radio access networks