Signal Denoising and Detection for Uplink in LoRa Networks Based on Bayesian-Optimized Deep Neural Networks

Abstract

Long-range and low-power communications are suitable technologies for the Internet of things networks. The long-range implies a very low signal-to-noise ratio at the receiver. In addition, low power consumption requires reduced signaling, hence the use of less complex protocols, such as ALOHA, so reduced communication coordination. Therefore, the increase of objects using this technology will automatically lead to an increase in interference. In this letter, we propose a detector for Long Range (LoRa) networks based on an autoencoder for denoising and dealing with the interference, followed by a convolutional neural network for symbol detection. Simulation results demonstrate that the proposed approach outperforms both the convolutional neural network-based detector and the classical LoRa detector in the presence of interference from other LoRa users. The proposed detector shows around 3 dB gain for a target Symbol Error Rate (SER) of 10−4.