# A light neural network for modulation detection under impairments

arXiv: https://arxiv.org/abs/2003.12260 · HTML: https://mlsanity.com/paper/2003.12260/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networks](https://mlsanity.com/paper/2211.13991/) (2022)
- [Compression of Recurrent Neural Networks using Matrix Factorization](https://mlsanity.com/paper/2310.12688/) (2023)
- [Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification](https://mlsanity.com/paper/2106.10543/) (2021)
- [Data-Driven Subsampling in the Presence of an Adversarial Actor](https://mlsanity.com/paper/2401.03488/) (2024)

## Abstract (arXiv)

We present a neural network architecture able to efficiently detect modulation scheme in a portion of I/Q signals. This network is lighter by up to two orders of magnitude than other state-of-the-art architectures working on the same or similar tasks. Moreover, the number of parameters does not depend on the signal duration, which allows processing stream of data, and results in a signal-length invariant network. In addition, we have generated a dataset based on the simulation of impairments that the propagation channel and the demodulator can bring to recorded I/Q signals: random phase shifts, delays, roll-off, sampling rates, and frequency offsets. We benefit from this dataset to train our neural network to be invariant to impairments and quantify its accuracy at disentangling between modulations under realistic real-life conditions. Data and code to reproduce the results are made publicly available.