Native Artificial Intelligence at the Physical Layer of 6G Networks: Foundations, Architectures and Implications for the Future Internet

The sixth generation of mobile networks (6G) represents a paradigmatic shift in the conception of wireless communication systems, where Artificial Intelligence (AI) is not integrated as an additional feature but is conceived as a native and fundamental component of the physical layer (PHY). This paper presents a comprehensive survey of the state of the art in AI-native physical layer for 6G, synthesizing approximately 100 references from the period 1948–2025. The survey systematically covers 5 main PHY components (channel coding, channel estimation, signal detection, beamforming, and semantic communications) and analyzes 8 AI architectural families (autoencoders, CNN, RNN/LSTM, Transformers, GNN, GAN, Diffusion Models, and Foundation Models), addressing theoretical foundations, proposed architectures, learning algorithms, implementation challenges, and future research directions. A rigorous mathematical framework underpinning these developments is presented, including optimization formulations, convergence analysis, and theoretical performance characterization. Published results from the literature demonstrate that AI-native physical layer can improve conventional performance metrics and enable emerging capabilities essential to 6G, such as semantic communications, predictive environmental adaptation, and operation in previously inaccessible computational complexity regimes. However, such gains are conditional on adequate training resources, robust channel-matched data, and careful consideration of known limitations including generalization across channel distributions, sample inefficiency, model interpretability, and hardware implementation constraints—all of which are critically analyzed in this survey. A reproducible proof-of-concept benchmark further confirms that, under severe resource constraints, autoencoder-based codes currently underperform conventional schemes, highlighting the gap between theoretical potential and practical deployment readiness.