Evaluating Performance of RAT Selection Algorithms for 5G Hetnets

Abstract

Next generation 5G cellular networks will consist of multiple technologies for devices to access the network at the edge. One of the keys to 5G is, therefore, the ability of devices to intelligently select its radio access technology (RAT). There have been several proposals for RAT selection in the last few years. Understanding the performance and limitation of these RAT selection solutions is important for their deployment in the future 5G heterogeneous networks. In this paper, we provide a taxonomy and comparative performance analysis of recent RAT selection algorithms, including the different network models that were used to evaluate these works. We combine these different network models to build a benchmark for evaluating the RAT selection algorithms in a 5G environment. We implement the representative algorithms of different approaches and cross compare them in our benchmark. From the experiments conducted, we illustrate how the different network parameters, such as the number of base stations visible to a user and the available link bandwidths, could impact the performance of these algorithms.