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Channel Impulse Response Analysis of the Indoor Propagation Based on Auto-Regressive Modeling

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Ad Hoc Networks (ADHOCNETS 2018)

Abstract

A novel statistical channel impulse response model at 2.6 GHz is proposed for the indoor stairs and corridor environment. The model is based on the frequency domain auto-regressive (AR) process. The samples of the complex frequency response can be described as the output of the AR transfer function driven by a Gaussian white-noise process. In this model, the number of poles of the AR transfer function is determined by the significant paths of radio propagation. The paths depend on the reflectors of different propagation environment. The accuracy of the AR modeling has been verified by utilizing the root-mean-square error and root-mean-square delay spread as metrics. The model is also compared with the conventional tapped delay line model. The proposed model can be useful for the development and design of future communication.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61701197 and 61571108, the Open Foundation of Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunication, Jiangsu Province, under Grant No. 2017WICOM01, the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University, under Grant No. 2018D15, the Open Foundation of State Key Laboratory of Networking and Switching Technology, the Fundamental Research Funds for the Central Universities under Grant No. JUSRP11742 and JUSRP11738, the Postgraduate Research & Practice Innovation Program of Jiangsu Provence under Grant No. SJCX18_0646.

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Correspondence to Jinpeng Liang .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Liang, J., Lu, W., Liu, Y., Wu, Q., Li, B., Li, Z. (2019). Channel Impulse Response Analysis of the Indoor Propagation Based on Auto-Regressive Modeling. In: Zheng, J., Xiang, W., Lorenz, P., Mao, S., Yan, F. (eds) Ad Hoc Networks. ADHOCNETS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-05888-3_26

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  • DOI: https://doi.org/10.1007/978-3-030-05888-3_26

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  • Online ISBN: 978-3-030-05888-3

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