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
Distinguished Professor
IEEE Fellow
AAIA Fellow
AAIS Fellow
School of Computer Sicence, Shanghai Jiao Tong University
Address: Room 437, Building 3, SEIEE No. 800 Dongchuan Road Minhang District, Shanghai, China
Email: FirstName.LastName AT sjtu.edu.cn
My research interests include Internet of Things, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics.