Architecture for Self-Evolution of 6G Core Network Based on Intelligent Decision Making

Abstract

The rapid progress of 6G mobile communication technologies has sparked a great deal research interests. The 6G core network architecture faces formidable challenges due to the escalating complexity of network service demands and diverse application scenarios. In response, our research endeavors to tackle these challenges by proposing a self-evolving architecture based on intelligent decision making. Inspired by the principles of biological morphological evolution, our architecture empowers the core network to dynamically adapt and reshape itself in order to effectively address the evolving communication environments. To facilitate this self-evolutionary process, we introduce a comprehensive framework encompassing mechanisms, architecture, agents, and algorithms that enable the network to autonomously generate and optimize its own structure, thereby ensuring adaptability to a wide range of application scenarios. By conducting concept proof simulation experiments, we have demonstrated the effectiveness of our self-evolution algorithm, which enables the 6G core network to make rational evolving decisions and exhibit remarkable adaptability to various application scenarios.

Keywords

• 6G core network
• self-evolution
• reinforcement learning