Two Applications of Deep Learning in the Physical Layer of Communication Systems
It addresses the potential applicability of deep learning in communication systems, but appears to be a conceptual or review piece rather than a novel contribution.
The paper questions the role of deep learning in communication systems, where signals are man-made and channels are well-modeled, contrasting it with its success in natural signal processing, but does not present specific results or numbers.
Deep learning has proved itself to be a powerful tool to develop data-driven signal processing algorithms for challenging engineering problems. By learning the key features and characteristics of the input signals, instead of requiring a human to first identify and model them, learned algorithms can beat many man-made algorithms. In particular, deep neural networks are capable of learning the complicated features in nature-made signals, such as photos and audio recordings, and use them for classification and decision making. The situation is rather different in communication systems, where the information signals are man-made, the propagation channels are relatively easy to model, and we know how to operate close to the Shannon capacity limits. Does this mean that there is no role for deep learning in the development of future communication systems?