A MOGA-Markov Chain Optimized Ranking Algorithm for Wireless Access Networks in Heterogeneous Environment

Abstract

The mounting customer demands for bandwidth-desirous services are deriving for a cost effective, robust, and high capacity wireless access network. The end users expect a satisfactory and economical delivery of “Quad-play” applications (voice, video, data, and mobility) and rich-media applications (multimedia, interactive gaming, and meta-verse) over the wireless network. In last two decades, a remarkable evolution of wireless networks is observed. Moreover, after the advent of software defined radio enabled wireless sets, the selection of the optimum wireless access network for different applications (live video streaming, online gaming, voice calling and browsing) is gaining vital importance. In this paper, a ranking algorithm based on Markov chain optimized learning approach is formulated for the heterogeneous environment. The algorithm is designed on the basis of most important Quality of Service (QoS) parameters like throughput, delay/error and cost. The proposed technique is robust against the change in number of available networks where, previously proposed techniques TOPSIS, VIKOR and RafoQ are unable to handle the change in available networks adequately. The Simulation results verify the selection of optimal access network for varying applications conforming to defined ranking algorithm.