An Optimal Load Balanced Resource Allocation Scheme for Heterogeneous Wireless Networks based on Big Data Technology

TL;DR

This paper proposes a big data-driven resource allocation scheme for heterogeneous wireless networks that optimizes radio access selection, improves service quality, reduces costs, and enhances roaming efficiency.

Contribution

It introduces a novel management method leveraging big data to optimize resource utilization and decision-making in heterogeneous wireless networks.

Findings

Improved accuracy in radio access technology selection.

Reduced roaming costs and request failures.

Enhanced resource distribution balance.

Abstract

An important issue in heterogeneous wireless networks is how to optimally utilize various radio resources. While many methods have been proposed for managing radio resources in each network, these methods are not suitable for heterogeneous wireless networks. In this study, a new management method is proposed which provides acceptable service quality and roaming rate, reduces the cost of the service, and utilizes big data technology for its operation. In our proposed scheme, by considering various parameters such as the type of the service, the information related to the location of the user, the movement direction of the user, the cost of the service, and a number of other statistical measures, the most suitable technology for radio access will be selected. It is expected that besides improving the decision making accuracy in selecting the radio access technology and the balanced distribution of network resources, the proposed method provides lower roaming and lower probability of stopping roaming requests entering the network. By considering the various service classes and various quality of service requirements regarding delay, vibration and so on, this can be useful in optimal implementation of heterogeneous wireless networks.