AI driven framework for optimized and resilient network slicing in 6G network

The advent of next-generation networks, such as Sixth Generation (6G, brings unprecedented demands for high security, ultra-low latency, exceptional reliability, and enhanced capacity. A critical enabler of these networks is reconfigurable wireless network slicing, which allows operators to deploy multiple virtualized network instances on a shared infrastructure, thereby enhancing Quality of Service (QoS). Achieving optimal QoS requires dynamic reconfiguration and optimization, which can be effectively realized through Artificial Intelligence (AI) and Machine Learning (ML) techniques. AI-driven solutions are pivotal in ensuring intelligent decision-making for network management and mitigating network slice failures. In this paper, we introduce an ensemble machine learning model to predict the optimal network slice. The proposed model’s performance is validated across diverse applications, including Smartphones, Smart Transportation, Smart Cities and Homes, Public Safety, Industry 4.0, Healthcare, and AR/VR/Gaming. Achieving an overall accuracy of 98.78%, the model demonstrates its potential to revolutionize network management in next-generation wireless systems.