AI-Driven Optimization Strategies for 6G Wireless Communication Systems: Advanced Architectures, Intelligent Algorithms, and Real-World Applications

Abstract

The sixth generation (6G) wireless communications systems are proposed to achieve transformative functionality, such as terabit per second data rates, sub-millisecond latency and seamless interconnection of terrestrial and non-terrestrial networks. These performance goals need smart, adaptive as well as cross-layer optimization, which is perfectly well suited to Artificial Intelligence (AI). This paper provides an overall survey and discussion on the application of AI-based optimization strategies in 6G networks. We discuss modern architectures, including AI-native core, edge-intelligent infrastructures or integrated satellite-terrestrial networks, and more algorithmic techniques to use represent deep learning to channel estimation, reinforcement learning to dynamic resource management, federated learning to privacy-aware optimization, and semantic communication to bandwidth-efficient transmission. With an envisioned AI-native optimization system, it is found in simulation that overall gains are substantial: a maximum 25 percent increase in signal-to-interference-plus-noise ratio (SINR), a 35 percent decrease in latency to URLLC services, and a 27 percent saving of energy in heterogeneous networks. The use in self-driving cars, the extended reality, smart factory, and telemedicine is mentioned. The paper ends with open challenges of scalability, energy efficiency, security and explainability and offers future research directions to focus deployment of resilient, intelligent and sustainable 6G communication systems.