mlsanityData to 7 Oct 2026

Model-Based Machine Learning for Communications

arXiv 2101.04726 ↗First posted 12 Jan 2021eess.SP cs.AI stat.MLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 45 references that are in the map. First posted on arXiv 12 Jan 2021.

Builds on

22 of 45 references.

References · 22

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10 more
  1. 2019ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detectioncited by 25
  2. 2020DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detectioncited by 23
  3. 2019Normalizing Flows: An Introduction and Review of Current Methodscited by 16
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  6. 2020Data-Driven Factor Graphs for Deep Symbol Detectioncited by 8
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Cited by year

Citing papers per year: 2021: 1; 2022: 5; 2023: 0; 2024: 1.

7 papers.

  1. 2022Model-Based Deep Learning: On the Intersection of Deep Learning and Optimizationcited by 50
  2. 2022Theoretical Perspectives on Deep Learning Methods in Inverse Problemscited by 22
  3. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  4. 2022Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysiscited by 6
  5. 2021Learn to Communicate with Neural Calibration: Scalability and Generalizationcited by 3
  6. 2022Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Accesscited by 3
  7. 2022Calibrating AI Models for Wireless Communications via Conformal Predictioncited by 0