Outage Probability Analysis of Multi-hop Relay Aided IoT Networks
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Published:2023-11-14
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ISSN:2032-9407
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Container-title:ICST Transactions on Scalable Information Systems
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language:
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Short-container-title:ICST Transactions on Scalable Information Systems
Author:
Wei Fusheng,Huang Jiajia,Zhao Jingming,Que Huakun
Abstract
This study delves into Internet of Things (IoT) networks wherein a transmitting source communicates information to a designated recipient. The presence of signal attenuation challenges the direct transmission of information from the source to the recipient. To surmount this obstacle, we investigate IoT network communication facilitated by multi-hop relays, whereby multiple relays collaboratively enable the conveyance of data from the source to the recipient across intermediate stages. For the considered IoT networks augmented by multi-hop relays, we assess the performance of the system by analyzing the probability of transmission outage. This analysis entails the derivation of an analytical expression for evaluating the occurrence of IoT network outage. Additionally, we gauge the system's effectiveness by examining the attainable transmission rate, wherein an analytical expression is furnished to assess the IoT data rate. The empirical results, along with the analytical findings, are subsequently presented to validate the formulated expressions in the context of IoT networks empowered by multi-hop relays. Notably, the utilization of multi-hop relaying emerges as a efficacious strategy for substantially expanding the coverage scope of IoT networks.
Publisher
European Alliance for Innovation n.o.
Subject
Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software
Reference32 articles.
1. Z. Li, J. Xie, W. Liu, H. Zhang, and H. Xiang, “Joint strategy of power and bandwidth allocation for multiple maneuvering target tracking in cognitive MIMO radar with collocated antennas,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 190–204, 2023. 2. T. Gafni, B. Wolff, G. Revach, N. Shlezinger, and K. Cohen, “Anomaly search over discrete composite hypotheses in hierarchical statistical models,” IEEE Trans. Signal Process., vol. 71, pp. 202–217, 2023. 3. A. Gupta, M. Sellathurai, and T. Ratnarajah, “End-to-end learning-based full-duplex amplify-and-forward relay networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 199– 213, 2023. 4. C. Chaieb, F. Abdelkefi, and W. Ajib, “Deep reinforcement learning for resource allocation in multi-band and hybrid OMA-NOMA wireless networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 187–198, 2023. 5. Z. Song, J. An, G. Pan, S. Wang, H. Zhang, Y. Chen, and M. Alouini, “Cooperative satellite-aerial-terrestrial systems: A stochastic geometry model,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 220–236, 2023.
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