AI-Native Self-Optimizing Architectures for Ultra-Reliable 6G Wireless Networks

The emergence of sixth-generation (6G) wireless networks is expected to enable ultra-reliable, low-latency communication (URLLC) for mission-critical applications such as autonomous systems, remote healthcare, and industrial automation. However, the increasing complexity, heterogeneity, and dynamic nature of next-generation networks pose significant challenges to conventional network management and optimization techniques. This paper proposes an AI-native self-optimizing architecture designed to address these challenges by embedding artificial intelligence at the core of 6G network operations. Unlike traditional add-on AI solutions, the proposed framework integrates machine learning models directly into the network control plane, enabling real-time monitoring, predictive analytics, and autonomous decision-making. The architecture leverages deep learning, reinforcement learning, and federated learning to dynamically optimize resource allocation, network slicing, interference management, and fault recovery. Furthermore, a digital twin-based network representation is incorporated to simulate and predict network behavior under varying conditions, thereby enhancing reliability and adaptability. The proposed system also emphasizes energy efficiency and scalability by utilizing lightweight AI models and edge intelligence. Simulation-based evaluations indicate significant improvements in network reliability, latency reduction, and spectral efficiency compared to conventional approaches. The results demonstrate that AI-native architectures can effectively transform 6G networks into intelligent, self-evolving systems capable of meeting stringent performance requirements. This work provides a comprehensive foundation for the development of fully autonomous wireless networks and highlights the critical role of artificial intelligence in shaping the future of ultra-reliable communication systems.