AI-Enabled Autoencoder-Based Physical Layer Design for 6G Communication Systems

Next-generation wireless communication 6G systems are expected to operate under diverse channel conditions and structures, requiring flexible and data-driven communication schemes. As traditional techniques face limitations in complex and dynamic environments, trained communication architectures have emerged as promising alternatives. In this paper, we present a thorough study on deep learning trained physical layer components, focusing on autoencoder-based transceivers and neural network modules that enhance the receiver’s intelligence. We further investigate two essential deep learning capabilities for modern receivers—modulation classification using neural architectures and generative data synthesis for channel estimation training. Moreover, the proposed models and simulation framework provide insight into how deep learning can be systematically integrated into the physical layer to improve adaptability, robustness, and efficiency.