Multiple virtual networks supported by 6G for scalable sports education teaching and training in virtual reality for Consumer applications

The advent of 6G networks has the potential to enable advanced virtual reality (VR) systems for scalable consumer-oriented sports education (SE) teaching and training. Scalability, real-time performance, and effective delivery of immersive consumer SE experiences are all problems for today's network architecture. Although VR is increasingly being used for remote consumer SE teaching, existing networks struggle to support numerous virtual networks while maintaining sufficient bandwidth and low latency for consumer-level applications. The research investigates the use of 6G-enabled multiple virtual networks to improve consumer SE teaching and training in VR. The goal is to create an optimal framework that facilitates real-time interactions while adapting and improving the virtual training experience for increased performance and scalability through the use of deep learning (DL) techniques, specifically designed with consumer usability in mind. The dataset is made up of consumer-generated movement sensor data, such as joint locations, angles, and velocities, as well as additional parameters like heart rate and calories burned. Data was preprocessed using Min-Max normalization to scale the features, ensuring compatibility with deep learning models and improving convergence. This data is used to train DL models such as Dynamic Walrus Optimized Intelligent Deep Belief Networks (DynWO-IntDBN) to evaluate and improve consumer SE in a VR scenario. DynWO-IntDBN models are used to examine and forecast consumer (student) performance in virtual environments. The suggested method optimizes resource allocation in 6G networks, ensuring continuous real-time interaction between instructors and consumer participants. The DynWO-IntDBN model has been improved to recognize specific physical actions and deliver actionable feedback tailored to consumer performance. The findings show that the framework performs well in terms of accuracy ratio (98.87%), performance ratio (97.4%), F1 score (95.8%), and efficiency ratio (95.9%). Therefore, the 6G-enabled virtual network infrastructure is a positive step forward for enhancing consumer engagement and effectiveness in sports education through immersive VR technologies.