Model-Based Machine Learning for Communications

Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

Research output: Working paper/PreprintPreprint

86 Downloads (Pure)


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.
Original languageEnglish
Number of pages40
StatePublished - 12 Jan 2021
Externally publishedYes


  • eess.SP
  • cs.AI
  • stat.ML


Dive into the research topics of 'Model-Based Machine Learning for Communications'. Together they form a unique fingerprint.

Cite this