Model-Based Machine Learning for Communications. (arXiv:2101.04726v1 [eess.SP])

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.



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