Analysis of AI-Enhanced Next-Generation Wireless Communication Systems

Abstract

The transition from 5G to 6G in mobile communication technology presents challenges, including achieving ultrahigh spectral efficiency, minimizing latency, and supporting large-scale connectivity. In dynamic and complex wireless settings, traditional model-driven methods are approaching their performance limits. Based on existing literature, this paper analyzes the core technical pathways through which artificial intelligence and machine learning can enhance 6G communications. The results indicate that, at the physical layer, data-driven deep learning models achieve accuracy levels comparable to or exceeding those of traditional methods in channel estimation and signal detection tasks, while simultaneously reducing pilot overhead. Meanwhile, reinforcement learning enables real-time intelligent optimization of massive multiple-input multipleoutput (MIMO) beam management, increasing spectral efficiency by 15-30%. At the network layer, federated learning serves as a distributed intelligence framework that safeguards data privacy. Its collaborative optimization approach improves model convergence, accelerating the process to twice the rate of conventional approaches. In addition, the semantic communication paradigm focuses on transmitting the meaning of information instead of raw bits, which reduces data volume by more than 80% in bandwidth-limited scenarios, thus boosting communication efficiency. The study further suggests that future networks will be AI-native, with AI deeply integrated into network architectures and protocols. This shift will transform the network’s role from a passive tool to an active AI-enabled service. poor model interpretability and high computational complexity, AI-driven enhancement remains a key pathway to advancing 6G performance.