Deep Learning Techniques for Advancing 6G Communications in the Physical Layer
Shangwei Zhang, Jiajia Liu, Tiago Koketsu Rodrigues, Nei Kato
Abstract
As current 5G communication systems cannot fulfill the stringent requirements brought by emerging applications, 6G will innovatively employ deep learning (DL) techniques to fundamentally rethink the communication systems design problem from the bottom to top layers. Although recent evidence has shown the power of DL techniques in the communication domain, the exploration and utilization of DL techniques in communication systems is still in its infancy and should come in a progressive manner. To effectively and efficiently implement DL techniques in future 6G communications in the physical layer, we give some potential deployment strategies and key enabling technologies that relate to 6G in terms of joint design of block-structured and end-to-end DL, integration of model-driven and data-driven DL, combination of online and offline training, ubiquitous learning and explainable DL techniques.