Enhancing LoRa Reception with Generative Models: Channel-Aware Denoising of LoRaPHY Signals

The proliferation of Internet of Things (IoT) applications relying on Low Power Wide Area Networks (LPWANs) demands robust and energy-efficient communication solutions. Among various LP-WAN technologies, LoRa emerges as a prominent choice due to its long-range capabilities and low energy consumption. However, the practical deployment of LoRa is hindered by significant signal degradation caused by channel and hardware noise, especially in urban environments. We introduce GLoRiPHY, a novel generative framework designed to enhance the reception quality of LoRaPHY signals through a channel-aware denoising mechanism. Utilizing a transformer-based architecture, GLoRiPHY leverages the known preamble of LoRaPHY signals to compensate for channel-induced distortions, thereby generating a clean signal suitable for direct demodulation. The system integrates Convolutional Neural Networks (CNNs) for efficient feature encoding and decoding, maintaining a compact model footprint even at higher Spreading Factors (SFs). Evaluations on real-world and simulated datasets show that in comparison to the current state-of-the-art solution, GLoRiPHY significantly lowers the Symbol Error Rate (SER) by up to 2.85x and demonstrates generalizability in unseen environments, while reducing inference times by up to 5.75x.