About Ganghui Lin Ganghui Lin M.S., Electrical and Computer Engineering IoT Deep learning Wireless Communications Ganghui is an MS student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ganghui obtained his bachelor’s degree in Electrical Engineering from the University of Electronic Science and Technology of China (UESTC) in 2022. He was a visiting student at CTL (July 2021 – July 2022) before joining KAUST. Research Interest Ganghui is focusing on the area of the next generation of wireless communication, the Internet of Things (IoT), and deep learning. Honors and Articles Related News April 2023 LoRa backscatter communications: Temporal, spectral, and error performance analysis 1 min read · Thu, Apr 27 2023 News LoRa backscatter (LB) communication systems can be considered as a potential candidate for ultra low power wide area networks (LPWAN) because of their low cost and low power consumption. In this paper, we comprehensively analyze LB modulation from various aspects, i.e., temporal, spectral, and error performance characteristics. First, we propose a signal model for LB signals that accounts for the limited number of loads in the tag. Then, we investigate the spectral properties of LB signals, obtaining a closed-form expression for the power spectrum. Finally, we derived the symbol error rate
LoRa backscatter communications: Temporal, spectral, and error performance analysis 1 min read · Thu, Apr 27 2023 News LoRa backscatter (LB) communication systems can be considered as a potential candidate for ultra low power wide area networks (LPWAN) because of their low cost and low power consumption. In this paper, we comprehensively analyze LB modulation from various aspects, i.e., temporal, spectral, and error performance characteristics. First, we propose a signal model for LB signals that accounts for the limited number of loads in the tag. Then, we investigate the spectral properties of LB signals, obtaining a closed-form expression for the power spectrum. Finally, we derived the symbol error rate
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