# NNCTC: Physical Layer Cross-Technology Communication via Neural Networks

arXiv: https://arxiv.org/abs/2403.10014 · HTML: https://mlsanity.com/paper/2403.10014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers](https://mlsanity.com/paper/1810.09082/) (2018)
- [Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks](https://mlsanity.com/paper/1810.07181/) (2018)
- [Performance Evaluation of Channel Decoding With Deep Neural Networks](https://mlsanity.com/paper/1711.00727/) (2017)
- [DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems](https://mlsanity.com/paper/2010.09268/) (2020)
- [An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks](https://mlsanity.com/paper/1906.06563/) (2019)

## Abstract (arXiv)

Cross-technology communication(CTC) enables seamless interactions between diverse wireless technologies. Most existing work is based on reversing the transmission path to identify the appropriate payload to generate the waveform that the target devices can recognize. However, this method suffers from many limitations, including dependency on specific technologies and the necessity for intricate algorithms to mitigate distortion. In this work, we present NNCTC, a Neural-Network-based Cross-Technology Communication framework inspired by the adaptability of trainable neural models in wireless communications. By converting signal processing components within the CTC pipeline into neural models, the NNCTC is designed for end-to-end training without requiring labeled data. This enables the NNCTC system to autonomously derive the optimal CTC payload, which significantly eases the development complexity and showcases the scalability potential for various CTC links. Particularly, we construct a CTC system from Wi-Fi to ZigBee. The NNCTC system outperforms the well-recognized WEBee and WIDE design in error performance, achieving an average packet reception rate(PRR) of 92.3% and an average symbol error rate(SER) as low as 1.3%.