A mean opinion score prediction model for VoIP calls offloading handover from LTE to WiFi

With the surge in mobile data traffic, leveraging Wi-Fi has become pivotal to alleviate the strain on LTE networks. An essential challenge lies in selecting the optimal Wi-Fi access point during Handover (HO), impacting Quality of Experience (QoE) and connection stability. This study introduces an innovative approach to enhance VoIP call Quality of Service (QoS) during HOs from LTE to Wi-Fi. Our contribution integrates two Mean Opinion Score (MOS) prediction models, multilayer perceptron (MLP) and recurrent neural networks (LSTMs) into the software-defined network controller (SDN), optimized by the Grey Wolf Optimizer (GWO). This solution provides accurate predictions for the SDN to forward mobile devices (MEs) and traffic to the nearest optimal Wi-Fi access points. Simulation results affirm the effectiveness of our approach. The LSTM-based approach performs well in the long run and provides better stability in terms of QoS parameters, especially when the metric of choice for mobile users is based on the Signal-to-Noise and Interference Ratio (SNIR), rather than the Received Signal Strength (RSSI) indicator. These results explore opportunities for the real-world deployment of the implemented models, particularly in urban areas experiencing cellular network congestion.