A Deep-Tree-Model-Based Radio Resource Distribution for 5G Networks

Abstract

Deep learning is a branch of machine learning that learns the high-level abstraction of data in a layered structure. Since its invention, it has been successfully applied in many image and speech processing applications. The success of deep learning depends on how big the data size is. Recently, the number of smart sensors and the Internet of Things have increased exponentially. This, in turn, has created huge traffic congestion in mobile and wireless communication networks. The available network resources need to be carefully utilized for seamless transmission of this large amount of data. Fortunately, deep learning performs very well with the big size of data. Therefore, the gap between machine learning research and advanced communication research should be narrowed down. In this article, we target an intelligent allocation of radio resources for 5G networks using deep learning. A framework consisting of a deep tree model and a long short-term memory network is proposed to predict future traffic congestion. Based on the prediction, the uplink and downlink ratio is adapted to utilize the resources optimally. Experimental results demonstrate that the proposed framework can achieve a low packet loss ratio and high throughput.