Deep Learning-based receiver for Uplink ...
Document type :
Communication dans un congrès avec actes
Title :
Deep Learning-based receiver for Uplink in LoRa Networks with Sigfox Interference
Author(s) :
Tesfay, Angesom Ataklity [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Institut de Recherche sur les Composants logiciels et matériels pour l'Information et la Communication Avancée - UAR 3380 [IRCICA]
Simon, Eric [Auteur]
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Kharbech, Sofiane [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Clavier, Laurent [Auteur]
Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Institut de Recherche sur les Composants logiciels et matériels pour l'Information et la Communication Avancée - UAR 3380 [IRCICA]
Simon, Eric [Auteur]

Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Kharbech, Sofiane [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Clavier, Laurent [Auteur]

Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Conference title :
IEEE 18th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2022
City :
Thessaloniki
Country :
Grèce
Start date of the conference :
2022-10-10
Book title :
2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)
English keyword(s) :
LoRa
IoT
deep learning
neural networks
capture effect
IoT
deep learning
neural networks
capture effect
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Apprentissage [cs.LG]
English abstract : [en]
The Internet of Things faces a significant scaling issue due to the rapid growth of the number of devices and asynchronous communications. Different technologies in the license-free industrial, scientific, and medical (ISM) ...
Show more >The Internet of Things faces a significant scaling issue due to the rapid growth of the number of devices and asynchronous communications. Different technologies in the license-free industrial, scientific, and medical (ISM) band have been widely deployed to fill this gap. LoRa and Sigfox are the most common. Many devices can use the ISM band if they obey the regulations and cope with internal and external interference. However, when there is massive connectivity, the effect of inter and intra-network interference between multiple networks is significant. This study uses a deep learning-based technique to decode signals and deal with the interference in the uplink of a LoRa network. Two classification-based symbol detection methods are proposed using a deep feedforward neural network (DFNN) and a convolutional neural network (CNN). The proposed receivers can decode the signals of a selected user when many LoRa users transmit simultaneously using the same spreading factor over the same frequency band (intra-spreading factor interference) and multiple Sigfox users interfere (internetwork interference). Simulation results show that both receivers outperform the conventional LoRa receiver in the presence of interference. For a target symbol error rate (SER) of 0.001, the proposed DFNN and CNN-based receivers attain around 2 dB and 3.5 dB gain, respectively.Show less >
Show more >The Internet of Things faces a significant scaling issue due to the rapid growth of the number of devices and asynchronous communications. Different technologies in the license-free industrial, scientific, and medical (ISM) band have been widely deployed to fill this gap. LoRa and Sigfox are the most common. Many devices can use the ISM band if they obey the regulations and cope with internal and external interference. However, when there is massive connectivity, the effect of inter and intra-network interference between multiple networks is significant. This study uses a deep learning-based technique to decode signals and deal with the interference in the uplink of a LoRa network. Two classification-based symbol detection methods are proposed using a deep feedforward neural network (DFNN) and a convolutional neural network (CNN). The proposed receivers can decode the signals of a selected user when many LoRa users transmit simultaneously using the same spreading factor over the same frequency band (intra-spreading factor interference) and multiple Sigfox users interfere (internetwork interference). Simulation results show that both receivers outperform the conventional LoRa receiver in the presence of interference. For a target symbol error rate (SER) of 0.001, the proposed DFNN and CNN-based receivers attain around 2 dB and 3.5 dB gain, respectively.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Source :
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