Poster Abstract: Neural Network-based OFDM/QAM Modulation for Wi-Fi-to-X Communication

Cross-Technology Communication (CTC) is a cornerstone for seamless interoperability in heterogeneous wireless environments, enabling diverse devices to coexist and cooperate effectively. In this paper, we present Wi-Fi-to-X, designed to leverage deep learning techniques to generate waveforms that are compatible with multiple communication protocols, allowing seamless data transmission between Wi-Fi and other wireless technologies such as ZigBee, LoRa, and Bluetooth. This approach enables devices operating under different wireless standards to communicate effectively without requiring hardware modifications or protocol standardization. By training a specialized neural network on simulations of Orthogonal Frequency Division Multiplexing (OFDM) and Quadrature Amplitude Modulation (QAM), we have improved the efficiency and reliability of signal processing in CTC, Wi-Fi-to-X achieves robust signal modulation and demodulation across disparate technologies, enabling communication from Wi-Fi to other IoT devices, including ZigBee, LoRa, and Bluetooth. We evaluated both USRP and commodity devices, demonstrated that Wi-Fi-to-X can achieve concurrent wireless communication from Wi-Fi to other IoT devices.