Novel Deep Learning Approach to Support Optimal Resource Allocation in 5G Environment

Abstract

In recent times, the advancement in network devices has focused entirely on the miniaturisation of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, we develop a resource allocation model using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.