# Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses

arXiv: https://arxiv.org/abs/2211.01131 · HTML: https://mlsanity.com/paper/2211.01131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning](https://mlsanity.com/paper/1712.06096/) (2017)
- [Drone classification from RF fingerprints using deep residual nets](https://mlsanity.com/paper/2011.13663/) (2020)
- [Experimental demonstration of a rapid sweep-tuned spectrum analyzer with temporal resolution based on a spin-torque nano-oscillator](https://mlsanity.com/paper/2004.03508/) (2020)

## Abstract (arXiv)

Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health. These RF inputs are composed of multiples frequencies. Here we show that magnetic tunnel junctions can process analogue RF inputs with multiple frequencies in parallel and perform synaptic operations. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals, using experimental data from magnetic tunnel junctions functioning as both synapses and neurons. We achieve the same accuracy as an equivalent software neural network. These results are a key step for embedded radiofrequency artificial intelligence.