Model-Based Machine Learning for Communications
This is an incremental review that provides guidelines for designing model-based deep learning systems in communications, targeting researchers and engineers in the field.
The paper reviews and compares strategies for integrating model-based algorithms with machine learning in communication systems, focusing on symbol detection to highlight the trade-offs between conventional deep learning, deep unfolding, and hybrid approaches.
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.