@inproceedings{6fddeb0821ec43a9adf69440af66bc09,
title = "A Meta Distribution-Based Fine-Grained Analysis for Contention-based WiFi Backscatter Networks",
abstract = "WiFi backscatter communication has gained many applications, but its performance characteristics remain to be analyzed. While existing research has investigated the success probability of backscatter tags in contention-based WiFi backscatter networks (CWBNs), it has focused solely on the first-order statistic of the signal-to-interference-plus-noise ratio (SINR). In this paper, we present a meta distribution-based fine-grained analysis that provides high-order statistics of SINR and characterizes the disparity among backscatter transmission links in CWBNs. Leveraging stochastic geometry, we, for the first time, derive mathematical expressions for the b-th moments of conditional success probability and its meta distribution. The extensive Monte-Carlo simulation results validate the accuracy of our proposed theoretical model and demonstrate its outstanding value to help us understand the overall performance of CWBNs.",
keywords = "Meta distribution, WiFi backscatter networks, stochastic geometry",
author = "Yulei Wang and Qinglin Zhao and Li Feng and Zhou, {Meng Chu} and Meng Shen and Yu Luo and Yi Sun",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st International Conference on Networking, Sensing and Control, ICNSC 2024 ; Conference date: 18-10-2024 Through 20-10-2024",
year = "2024",
doi = "10.1109/ICNSC62968.2024.10760116",
language = "English (US)",
series = "ICNSC 2024 - 21st International Conference on Networking, Sensing and Control: Artificial Intelligence for the Next Industrial Revolution",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICNSC 2024 - 21st International Conference on Networking, Sensing and Control",
address = "United States",
}