论文标题
深度学习的两个应用在通信系统的物理层中
Two Applications of Deep Learning in the Physical Layer of Communication Systems
论文作者
论文摘要
深度学习已证明自己是开发数据驱动的信号处理算法的强大工具，以挑战工程问题。通过学习输入信号的关键特征和特征，而不是要求人类先识别和建模它们，而是可以击败许多人造算法。特别是，深度神经网络能够学习自然信号（例如照片和录音）中的复杂功能，并将其用于分类和决策。 在通信系统中，信息信号是人为的，传播渠道相对易于建模，我们知道如何接近香农容量限制。这是否意味着深度学习在未来的交流系统的发展中没有作用？
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?
