# On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals

arXiv: https://arxiv.org/abs/2303.06438 · HTML: https://mlsanity.com/paper/2303.06438/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation](https://mlsanity.com/paper/2007.13975/) (2020)
- [Sudo rm -rf: Efficient Networks for Universal Audio Source Separation](https://mlsanity.com/paper/2007.06833/) (2020)
- [Asteroid: the PyTorch-based audio source separation toolkit for researchers](https://mlsanity.com/paper/2005.04132/) (2020)
- [Deep neural networks for single channel source separation](https://mlsanity.com/paper/1311.2746/) (2013)
- [Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation](https://mlsanity.com/paper/2208.10325/) (2022)
- [Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals](https://mlsanity.com/paper/2209.04871/) (2022)

## Built on by (most cited first, 3 of 3)

- [RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge](https://mlsanity.com/paper/2409.08839/) (2024)
- [The Radio-Frequency Transformer for Signal Separation](https://mlsanity.com/paper/2603.09201/) (2026)
- [Advancing AI Challenges for the United States Department of the Air Force](https://mlsanity.com/paper/2511.00267/) (2025)

## Abstract (arXiv)

We study the single-channel source separation problem involving orthogonal frequency-division multiplexing (OFDM) signals, which are ubiquitous in many modern-day digital communication systems. Related efforts have been pursued in monaural source separation, where state-of-the-art neural architectures have been adopted to train an end-to-end separator for audio signals (as 1-dimensional time series). In this work, through a prototype problem based on the OFDM source model, we assess -- and question -- the efficacy of using audio-oriented neural architectures in separating signals based on features pertinent to communication waveforms. Perhaps surprisingly, we demonstrate that in some configurations, where perfect separation is theoretically attainable, these audio-oriented neural architectures perform poorly in separating co-channel OFDM waveforms. Yet, we propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures, that can confer about 30 dB improvement in performance.