Dynamic Resource Prediction and Allocation in C-RAN With Edge Artificial Intelligence

Abstract

Artificial intelligence is one of the important technologies for industrial applications, but it needs a lot of computing resources and sensing data to support. Therefore, big data transmission is a challenge for current network architectures. In order to have high-performance computing requirements, this paper proposes an emerging network architecture that combines edge computing and cloud computing to reduce the transmission of useless data and solve bottleneck problems. Moreover, we define the resource allocation problem about multiple remote radio heads and multiple baseband unit pools in the cloud radio access network for fifth generation. The long short-term memory is used to predict dynamic throughput and genetic algorithm based resource allocation algorithm is used to optimize resource allocation. The simulation results represented that the proposed mechanism can achieve high resource utilization and reduce power consumption.