Parallel Decoding in LoRaWANs
This project presents a novel communication paradigm that enables parallel demodulation of colliding LoRa transmissions. We resolve LoRa collisions at the physical layer and thereby support parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features.
This work has been published in SenSys 2019.
Tao Gu
Professor
IEEE Fellow
AAIA Fellow
ACM Distinguished Member
Email: firstname dot lastname AT mq.edu.au
Phone: +61-2-9850-4357
Address: Room 267, 4 Research Park Drive, North Ryde, NSW 2109, Australia
My research interests include Internet of Things, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics.