A Channel Attention-Based Denoising Autoencoder U-Net and Classifier (UNA-DAEC) for Signal Identification Under Challenging SNR Conditions

The use of Low-Power Wide Area Network (LPWAN) technologies, such as Long Range (LoRa), Sigfox, and IEEE 802.15.4g (ZigBee), has grown significantly, addressing a wide range of applications including smart metering, agriculture, smart homes, and healthcare. These technologies are valued for their simplicity, flexible connectivity, low power consumption, efficient modulation techniques, and moderate data rates. As a result, they can coexist within the same environment, serving either similar or distinct applications. However, the increasing deployment of devices and technologies has amplified the likelihood of interference between them, leading to performance degradation, particularly in real-world scenarios under challenging conditions where noise power surpasses signal power. The rapid proliferation of these technologies, especially within unlicensed Industrial, Scientific, and Medical (ISM) frequency bands, underscores the need for effective techniques to ensure seamless coexistence without disrupting communication. To address this challenge, we investigate the role of data representation and propose a Channel Attention-based Denoising Autoencoder U-Net and Classifier (UNA-DAEC). This model is designed to denoise multi-label LPWAN signals affected by white Gaussian noise and accurately classify overlapping transmissions, specifically IEEE 802.15.4g, Sigfox, and LoRa signals, within the same environment. The primary objective of UNA-DAEC is to achieve reliable signal classification in low Signal-to-Noise Ratio (SNR) conditions. This is achieved by first denoising the noisy signals to obtain optimal representations, enabling high classification accuracy with a single forward and backward propagation. Our results further demonstrate that data representation plays a critical role in identifying and classifying LPWAN signals, particularly in challenging low-SNR environments, with a significant performance of 44%, 7%, and 26% over CNN-based IQ, CNN-based FFT and DAE+Classifier methods, respectively, at -10 dB SNR.