Physical Layer Cross-Technology Communication via Explainable Neural Networks

Abstract

Cross-technology communication (CTC) facilitates seamless interaction between different wireless technologies. Most existing methods use reverse engineering to derive the required transmission payload, generating a waveform that the target device can successfully demodulate. However, traditional approaches have certain limitations, including reliance on specific reverse engineering algorithms or the need for manual parameter tuning to reduce emulation distortion. In this work, we present NNCTC, a framework for achieving physical layer cross-technology communication through explainable neural networks, incorporating relevant knowledge from the wireless communication physical layer into the neural network models. We first convert the various signal processing components within the CTC process into neural network models, then build a training framework for the CTC encoder-decoder structure to achieve CTC. NNCTC significantly reduces the complexity of CTC by automatically deriving CTC payloads through training. We demonstrate how NNCTC implements CTC in WiFi systems using OFDM and CCK modulation. On WiFi systems using OFDM modulation, NNCTC outperforms the WEBee and WIDE designs in terms of error performance, achieving an average packet reception ratio (PRR) of 92.3% and an average symbol error rate (SER) as low as 1.3%. In WiFi systems using OFDM modulation, the highest PRR can reach up to 99%.