Reg-GelNet: Deep Learning Receiver for Asynchronous Distributed Chirp Spread Spectrum

Abstract

Asynchronous distributed chirp spread spectrum (AD-CSS) enables cooperative long-range, low-power Internet of Things (IoT) communication but suffers from independent time, frequency, and phase offsets, leading to non-coherent signal addition and degraded detection performance. Conventional receivers relying on explicit offset and channel estimation become computationally burdensome and inaccurate in such settings. To address this, we propose Reg-GelNet, a deep learning–based receiver that performs offset and channel compensation along with symbol detection. Reg-GelNet uses a deep neural network (DNN) to mitigate signal impairments, a Gel’fand transform for compact time–frequency feature extraction, and a convolutional neural network (CNN) for symbol classification, removing the need for explicit synchronization or channel estimation. Simulations show that Reg-GelNet achieves markedly lower bit error rate (BER) than state-of-the-art receivers while maintaining low complexity, offering an efficient and practical solution for asynchronous IoT networks.