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Communication Dans Un Congrès Année : 2021

Deep Learning-based Signal Detection for Uplink in LoRa-like Networks

Résumé

The increasing number of devices together with uncoordinated transmissions result in a major challenge of scalability in the Internet of things. This paper deals with signal detection in the uplink of a LoRa network through a deep learning-based approach. Two strategies are proposed: regression for bit detection based on a deep feedforward neural network and classification for symbol detection based on a convolutional neural network. These receivers can decode a selected user's signals when multiple users simultaneously transmit over the same frequency band with the same spreading factor. Simulation results show that both receivers outperform the classical LoRa one in the presence of interference. The results show that the introduced approach is relevant to deal with the scalability issue.
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Dates et versions

hal-03373813 , version 1 (11-10-2021)

Identifiants

  • HAL Id : hal-03373813 , version 1

Citer

Angesom Ataklity Tesfay, Eric Pierre Simon, Sofiane Kharbech, Laurent Clavier. Deep Learning-based Signal Detection for Uplink in LoRa-like Networks. IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021, Sep 2021, Helsinki, Finland. pp.617-621. ⟨hal-03373813⟩
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