AI Driven 6G Network Slicing for Distance Collaborative Sports Training: Edge Cloud Resource Allocation Strategy

The rapid evolution of communication networks has paved the way for developing 6G technology, which promises ultra-low latency, massive connectivity, and enhanced intelligence. In distance collaborative sports training, where athletes and coaches operate remotely, real-time data transmission, seamless connectivity, and adaptive network management are crucial. It presents an AI-driven 6G network slicing approach integrated with deep learning techniques to enhance real-time performance tracking, and enable virtual coaching in collaborative basketball training. A novel Adaptive Pelican Optimized - Elman Spike Neural Network (APO-ESNN) model is employed to dynamically allocate resources based on real-time demand, ensuring efficient training sessions. The edge cloud in 6G sports training facilitates low-latency processing, AI-driven analytics, and optimal bandwidth allocation, enabling seamless communication, individualized coaching, and effective resource utilization to improved athlete performance. In a 6G environment, multiple network slices optimize sports training. The video streaming slice handles real-time video, the sensor data slice ensures low-latency wearable data transmission, and the AI processing slice analyzes sensor data for individualized coaching. Customization adjusts slices based on training requirements. The experimental framework was developed using Python software (version 3.10), and achieved a resource allocation efficiency of 92% and a training session efficiency of 85%, demonstrating significant improvements in network utilization and session productivity. This AI-driven, data-powered basketball training system highlights the transformative potential of 6G network slicing in revolutionizing collaborative sports training while advancing real-time sports analytics and interactive training experiences.