Optimal Wireless Network Selection Based on Machine Learning for Reliable Communication in Cybernetic Avatar Remote Control

Abstract

In this study, we investigate the optimization of wireless network selection for reliable communication in cybernetic avatar remote control. A centralized method utilizing a feedforward neural network is proposed to address the limitations of traditional methods, which rely on single metrics such as received signal strength indicator and are reactive. By integrating network-wide information and mobility data, the method predicts throughput in real time and selects the optimal network connection to ensure stable and high-quality communication. Experimental evaluations demonstrated up to 2-fold improvement in average throughput compared with the signal-based network selection method, while maintaining comparable processing times. The proposed method highlights the potential of a machine learning-based network selection method to enhance real-time QoS in dynamic environments.