An optimal multitier resource allocation of cloud RAN in 5G using machine learning

The networks are evolving drastically since last few years in order to meet user requirements. For example, the 5G is offering most of the available spectrum under one umbrella. In this work, we will address the resource allocation problem in fifth‐generation (5G) networks, to be exact in the Cloud Radio Access Networks (C‐RANs). The radio access network mechanisms involve multiple network topologies that are isolated based on the spectrum bands and it should be enhanced with numerous access technology in the deployment of 5G network. The C‐RAN is one of the optimal technique to combine all the available spectral bands. However, existing C‐RAN mechanisms lacks the intelligence perspective on choosing the spectral bands. Thus, C‐RAN mechanism requires an advanced tool to identify network topology to allocate the network resources for substantial traffic volumes. Therefore, there is a need to propose a framework that handles spectral resources based on user requirements and network behavior. In this work, we introduced a new C‐RAN architecture modified as multitier Heterogeneous Cloud Radio Access Networks in a 5G environment. This architecture handles spectral resources efficiently. Based on the simulation analysis, the proposed multitier H‐CRAN architecture with improved control unit in network management perspective enables augmented granularity, end‐to‐end optimization, and guaranteed quality of service by 15 percentages over the existing system.

This paper summarizes research findings based on C‐RAN as mentioned below,