Signal Denoising and Detection for Uplink in LoRa Networks based on Bayesian-optimized Deep Neural Networks

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Tesfay, Angesom Ataklity | Kharbech, Sofiane | Simon, Eric Pierre | Clavier, Laurent

Edité par HAL CCSD ; Institute of Electrical and Electronics Engineers

International audience. 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 paper, we propose a detector for Long Range (LoRa) networks based on an for denoising and dealing with the interference, followed by a 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.

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