Self-Learning and Adaptive Networking Protocols and Algorithms for 6G Edge Nodes

Edge intelligence is a new technical framework that seeks to unify Artificial Intelligence (AI), mobile edge computing, and communications networks. It is widely recognized as one of the most important missing pieces of the present 5G network, and it projected to be one of the most aspects to next-generation 6G edge-AI. This article provides a self-learning infrastructure centered on self-supervised generative adversarial networks to illustrate how automated data synthesis and learning at the networking edge might boost performance. Our 5G-connected campus shuttle system serves as a test bed for our planned self-learning architecture. The results we obtained show that the suggested architecture may successfully identify and categorize novel services in edge computing settings.