# Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification

arXiv: https://arxiv.org/abs/2106.10543 · HTML: https://mlsanity.com/paper/2106.10543/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 19 Jun 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Deep Unfolding for Communications Systems: A Survey and Some New Directions](https://mlsanity.com/paper/1906.05774/) (2019)
- [SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python](https://mlsanity.com/paper/1907.10121/) (2019)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Fast Deep Learning for Automatic Modulation Classification](https://mlsanity.com/paper/1901.05850/) (2019)
- [Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder](https://mlsanity.com/paper/2011.08295/) (2020)
- [Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention](https://mlsanity.com/paper/1608.06409/) (2016)
- [DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems](https://mlsanity.com/paper/2010.09268/) (2020)
- [A Learnable Distortion Correction Module for Modulation Recognition](https://mlsanity.com/paper/1803.01319/) (2018)
- [A light neural network for modulation detection under impairments](https://mlsanity.com/paper/2003.12260/) (2020)
- [Ensemble Wrapper Subsampling for Deep Modulation Classification](https://mlsanity.com/paper/2005.04586/) (2020)
- [Learning Constellation Map with Deep CNN for Accurate Modulation Recognition](https://mlsanity.com/paper/2009.02026/) (2020)
- [DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network](https://mlsanity.com/paper/2011.11337/) (2020)
- [Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems](https://mlsanity.com/paper/1605.00716/) (2016)
- [Deep Learning for Over-the-Air Non-Orthogonal Signal Classification](https://mlsanity.com/paper/1911.06174/) (2019)
- [Training Data Augmentation for Deep Learning Radio Frequency Systems](https://mlsanity.com/paper/2010.00178/) (2020)
- [Learning Approximate Neural Estimators for Wireless Channel State Information](https://mlsanity.com/paper/1707.06260/) (2017)
- [Blind Channel Estimation and Data Detection with Unknown Modulation and Coding Scheme](https://mlsanity.com/paper/1909.11306/) (2019)
- [Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems](https://mlsanity.com/paper/2004.00378/) (2020)
- [Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding](https://mlsanity.com/paper/2006.00729/) (2020)
- [Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach](https://mlsanity.com/paper/2008.06720/) (2020)

## Abstract (arXiv)

Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose the dual path network (DPN). It consists of a signal path of DSP operations that recover the signal, and a feature path of neural networks that estimate the unknown transmit parameters. By interconnecting the paths over several recovery stages, later stages benefit from the recovered signals and reuse all the previously extracted features. The proposed design is demonstrated to provide 5% improvement in modulation classification compared to alternative designs lacking either feature sharing or access to recovered signals. The estimation results of DPN along with its blind decoding performance are shown to outperform a blind signal processing algorithm for BPSK and QPSK on a simulated dataset. An over-the-air software-defined-radio capture was used to verify DPN results at high SNRs. DPN design can process variable length inputs and is shown to outperform relying on fixed length inputs with prediction averaging on longer signals by up to 15% in modulation classification.