NNCTC: Physical Layer Cross-Technology Communication via Neural Networks
This work addresses the challenge of enabling seamless interactions between diverse wireless technologies, offering a scalable solution that reduces development complexity, though it is incremental as it builds on existing CTC methods.
The paper tackles the problem of cross-technology communication (CTC) between wireless technologies by proposing NNCTC, a neural-network-based framework that autonomously derives optimal payloads through end-to-end training without labeled data, achieving a 92.3% packet reception rate and 1.3% symbol error rate in a Wi-Fi to ZigBee system.
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%.