Load balancing using neural networks approach for assisted content delivery in heterogeneous network

Abstract

Improving flexibility and adding complexity are the most driving terms in any computer or telecommunications network and should be carefully considered when proposing new technical solutions for the next (5G) networks. Until the full release of the next generation mobile network; 4G, represented by LTE-A, will continue to develop and improve with new releases launched periodically; each of them demonstrates new added feature(s) that can be seen to continue working with 5G, such enhancing features include small cells in heterogeneous networks, cloud computing, virtualization, software defined networks (SDN), content delivery network (CDN) and cloud radio access networks (CRAN). Resource allocation in cloud computing is one of the major challenges in such systems; this is due to users’ frequent mobility, hence the need for an efficient load balancing method assisted by prediction trait. In this paper we propose and discuss load balancing technique using Hopfield artificial neural network and Radial Basis Function Neural Network for content delivery mechanism in Heterogeneous LTE-A mobile network.