We present an introduction to model-based machine learning for communication systems. We begin by reviewing existing strategies for combining model-based algorithms and machine learning from a high level perspective, and compare them to the conventional deep learning approach which utilizes established deep neural network (DNN) architectures trained in an end-to-end manner. Then, we focus on symbol detection, which is one of the fundamental tasks of communication receivers. We show how the different strategies of conventional deep architectures, deep unfolding, and DNN-aided hybrid algorithms, can be applied to this problem. The last two approaches constitute a middle ground between purely model-based and solely DNN-based receivers. By focusing on this specific task, we highlight the advantages and drawbacks of each strategy, and present guidelines to facilitate the design of future model-based deep learning systems for communications.
翻译:我们介绍了基于模型的通信系统机器学习的导言。我们首先审查现有战略,以便从高层次的角度将基于模型的算法和机器学习结合起来,并将这些战略与传统的深层次学习方法进行比较,后者利用以端对端方式培训的既定深层神经网络(DNN)结构。然后,我们侧重于符号探测,这是通信接收者的基本任务之一。我们展示了传统深层结构、深层发展和DNN辅助混合算法的不同战略如何适用于这一问题。最后两种方法构成了纯基于模型的和纯基于DNNN的接收器之间的中间地带。我们侧重于这一具体任务,强调了每项战略的优点和缺点,并提出了指导方针,以便利设计基于未来基于模型的深层通信系统。