Intelli-GATS: Dynamic Selection of the Wi-Fi Multicast Transmission Policy Using Interpretable-AI

COVID-19 has changed the way we use networks, as multimedia content now represents an even more significant portion of traffic due to rise in remote education and telecommuting. In this context, which Wi-Fi is predominant radio access technology (RAT), multicast transmissions have become a reduce overhead network when many users same content. However, lacks versatile transmission method for ensuring efficiency, scalability, reliability. Although IEEE 802.11aa amendment defines different operation modes, these perform well only particular situations do not adapt channel conditions. Moreover, methods dynamically adapting them situation exist. view shortcomings, artificial intelligence (AI) machine learning (ML) emerged solutions automating management. most accurate models usually operate black boxes, triggering mistrust among human experts. Accordingly, research efforts moved towards using Interpretable-AI that humans can easily track. Thus, work presents solution designed select best mode improve scalability efficiency kind transmission. The evaluation shows our approach outperforms standard by up 38%.