Adaptive neuro-fuzzy inference models for speech and video quality prediction in real-world mobile communication networks

Abstract

This article presents a unified Quality of Service (QoS) prediction methodology based on neuro-fuzzy inference systems that can be used in contemporary rollout mobile communication networks. Our work is concentrated on radio key performance indicators of mobile radio access networks that affect speech and video quality of wireless multimedia communications, and on how we can estimate Quality of end-user Experience (QoE) using fuzzy-based techniques. We propose a methodology that is based on modern experimental drive-test equipment with which a measurement campaign is configured and conducted in various environments. Afterwards, ANFIS models are developed based on real network measurements and numerical results are presented.