Model-Based Machine Learning for Communications

• URL: http://arxiv.org/abs/2101.04726v1
• Date: Tue, 12 Jan 2021 19:55:34 GMT
• Title: Model-Based Machine Learning for Communications
• Authors: Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, and Andrea J. Goldsmith
• Abstract summary: We review existing strategies for combining model-based algorithms and machine learning from a high level perspective. We focus on symbol detection, which is one of the fundamental tasks of communication receivers.
• Score: 110.47840878388453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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