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Tang, W., Chen, M., Chen, X., Dai, J., Han, Y., Di Renzo, M., Zeng, Y., Jin, S., Cheng, Q. and Cui, T. (2020) Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement. IEEE Transactions on Wireless Communications, 20, 421-439.
https://doi.org/10.1109/TWC.2020.3024887

has been cited by the following article:

• TITLE: Measurement and Modeling of LoRa Signal in Multi-Floor Home Environment

  AUTHORS: Chen Zhong

  KEYWORDS: LoRa, Smart Home, Path Loss Mode, Rician, Rayleigh, Packet Reception Rate

  JOURNAL NAME: Communications and Network, Vol.17 No.1, February 28, 2025

  ABSTRACT: The Internet of Things (IoT) is rapidly developing with the promotion of new technologies such as LoRa, which offers extensive coverage, low power consumption, and strong anti-interference capabilities. This study focuses on the application of LoRa technology in multi-floor home environments, particularly addressing the challenges of signal multipath propagation. We conducted comprehensive measurements of LoRa signal strength and path loss across different floors and rooms. Through our path loss model analysis, notable differences were observed in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, with initial path loss values of 58.32 decibels and 51.52 decibels, respectively, and standard deviations of 18.42 decibels for LOS and 2.84 decibels for NLOS. Temporal fading analysis, using Rayleigh and Rician distributions, revealed significant variations in signal strength between daytime and nighttime, with some rooms being more stable during the daytime and others more stable at nighttime due to differences in the architectural structure and functionality of various rooms within the home environment. Packet reception rate (PRR) ranged from 89.07% to 99.89%, highlighting the reliability of data transmission under different conditions. This research fills a critical gap in the literature by providing empirical data on indoor multi-floor home environments and significantly contributes by verifying and modeling path loss and temporal fading, thereby improving the design and deployment strategies for LoRa-based smart home systems.