Music Transmission and Performance Optimization Based on the Integration of Artificial Intelligence and 6G Network Slice

Abstract

Network slicing, which enables efficient resource management to meet specific service requirements, provides a scalable solution for optimizing music transmission and live performance in mobile networks beyond 5G and into 6G. The research focuses on optimizing live performances as well as music transmission. Since AI-driven resource management improves performance quality and real-time music streaming in dynamic 6G network situations, these factors are interconnected. This approach allows multiple tenants, such as event organizers and music producers, to share infrastructure while customizing communication and quality standards for real-time music services. To ensure optimal resource allocation, including high bandwidth, low latency, and consistent service quality, network slices are dynamically configured by the infrastructure provider. Although the implementation of network slicing in the core network has been well studied, applying it within the radio access network (RAN) presents challenges, especially given the unpredictability of wireless channels and the strict quality of service (QoS) demands for live music streaming. For 6G networks, the article suggests a tenant-driven RAN slicing method improved by artificial intelligence (AI) to maximize music performance and transmission. A hybrid AI framework integrates a deep recurrent neural network (DRNN) for continuous monitoring and prediction of network conditions with a deep Q-network (DQN) augmented by prioritized experience replay (PER) for real-time resource adaptation. The DRNN forecasts network states to guide high-level resource allocation, whereas DQN with PER dynamically manages routing and bandwidth based on past critical network states, enabling rapid responses to fluctuating performance demands. Comparative results indicate that the suggested approach outperforms conventional techniques, achieving a latency of 25 ms, an audio quality of 4.6, and a bandwidth utilization of 90%. Simulation results in live music and enhanced mobile broadband (eMBB) environments demonstrate the proposed approach's effectiveness in minimizing latency, enhancing audio quality, and stabilizing transmission, surpassing traditional network allocation techniques.