  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%.
