Abstract
AbstractThe advent of intelligent reflecting surface (IRS) technology has revolutionized the landscape of wireless communication systems, offering promising opportunities for enhancing the performance of Internet of Things (IoT) applications. This paper presents a comprehensive performance evaluation of multi-agent IoT monitoring systems leveraging IRS technology. We focus on three criteria for selecting IRS units and assess the impact on system performance. Specifically, we analyze the system performance by deriving an outage probability expression for each criterion. Our study begins by introducing the concept of IRS and its role in IoT monitoring. We then present three IRS unit selection criteria: optimal selection (OS), partial selection (PS), and random selection (RS). For each criterion, we mathematically model and analyze the system outage probability, shedding light on the reliability and connectivity of IoT devices. The outage probability expressions derived in this work offer valuable insights into the trade-offs associated with IRS unit selection criteria in the context of IoT monitoring. Additionally, our findings contribute to the optimization of multi-agent IoT monitoring systems, enabling improved communication performance and enhanced reliability.
Publisher
Springer Science and Business Media LLC
Reference27 articles.
1. Z. Huang, L. Bai, X. Cheng, X. Yin, P.E. Mogensen, X. Cai, A non-stationary 6g V2V channel model with continuously arbitrary trajectory. IEEE Trans. Veh. Technol. 72(1), 4–19 (2023)
2. X. Liu, C. Sun, M. Zhou, C. Wu, B. Peng, P. Li, Reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion for industrial big spectrum data. IEEE Trans. Ind. Inform. 17(5), 3391–3400 (2021)
3. Y. Wu, S. Tang, L. Zhang, Resilient machine learning based semantic-aware MEC networks for sustainable next-G consumer electronics. IEEE Trans. Consum. Electron. PP(99), 1–10 (2023)
4. F.L. Andrade, M.A.T. Figueiredo, J. Xavier, Distributed Banach-Picard iteration: application to distributed parameter estimation and PCA. IEEE Trans. Signal Process. 71, 17–30 (2023)
5. Z. Na, Y. Liu, J. Shi, C. Liu, Z. Gao, Uav-supported clustered NOMA for 6g-enabled internet of things: trajectory planning and resource allocation. IEEE Internet Things J. 8(20), 15041–15048 (2021)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献