# Model-Based Machine Learning for Communications

arXiv: https://arxiv.org/abs/2101.04726 · HTML: https://mlsanity.com/paper/2101.04726/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 22 of 22)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures](https://mlsanity.com/paper/1409.2574/) (2014)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [A Survey of Deep Learning Techniques for Neural Machine Translation](https://mlsanity.com/paper/2002.07526/) (2020)
- [Deep Unfolding for Communications Systems: A Survey and Some New Directions](https://mlsanity.com/paper/1906.05774/) (2019)
- [Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks](https://mlsanity.com/paper/1903.00954/) (2019)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection](https://mlsanity.com/paper/2002.03214/) (2020)
- [Neural Network Detection of Data Sequences in Communication Systems](https://mlsanity.com/paper/1802.02046/) (2018)
- [Normalizing Flows: An Introduction and Review of Current Methods](https://mlsanity.com/paper/1908.09257/) (2019)
- [Deep Learning for Image Denoising: A Survey](https://mlsanity.com/paper/1810.05052/) (2018)
- [Data-Driven Factor Graphs for Deep Symbol Detection](https://mlsanity.com/paper/2002.00758/) (2020)
- [Radar and Communication Co-existence: an Overview](https://mlsanity.com/paper/1902.08676/) (2019)
- [End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1901.08570/) (2019)
- [Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks](https://mlsanity.com/paper/1604.07176/) (2016)
- [Model-Driven Deep Learning for MIMO Detection](https://mlsanity.com/paper/1907.09439/) (2019)

## Built on by (most cited first, 8 of 8)

- [Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization](https://mlsanity.com/paper/2205.02640/) (2022)
- [Theoretical Perspectives on Deep Learning Methods in Inverse Problems](https://mlsanity.com/paper/2206.14373/) (2022)
- [Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach](https://mlsanity.com/paper/2406.02616/) (2024)
- [Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis](https://mlsanity.com/paper/2207.10939/) (2022)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)
- [Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access](https://mlsanity.com/paper/2205.00849/) (2022)
- [Statistically Valid Post-Training Hyperparameter Selection: From Tuning to Guarantees](https://mlsanity.com/paper/2606.25601/) (2026)
- [Calibrating AI Models for Wireless Communications via Conformal Prediction](https://mlsanity.com/paper/2212.07775/) (2022)

## 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.