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?

通过深度学习算法学习输入信号的关键特征和特性，而不需要人为进行先前的识别和建模，深度神经网络能够学习自然制造信号（例如照片和音频）中的复杂特征，并将其用于分类和决策，但在通信系统中，情况有所不同。信息信号是人造的且传播渠道相对易于建模以及在香农容量限制附近操作，那么深度学习在未来通信系统的发展中是否有作用呢？