Research on Wireless Fading Characteristic in Urban Bridge Environment of the Inland Waterway Based on Channel Measurement
International Journal of Wireless Communications and Mobile Computing
Volume 7, Issue 2, December 2019, Pages: 38-47
Received: Jan. 28, 2020; Accepted: Feb. 13, 2020; Published: Feb. 20, 2020
Views 382      Downloads 78
Authors
Jing Zhang, School of Automation, Wuhan University of Technology, Wuhan, China
Changzhen Li, School of Information Engineering, Wuhan University of Technology, Wuhan, China
Xuanhao Shu, School of Information Engineering, Wuhan University of Technology, Wuhan, China
Wei Chen, School of Information Engineering, Wuhan University of Technology, Wuhan, China
Article Tools
Follow on us
Abstract
This paper focuses on the fading characteristics of a wireless channel of an inland waterway in an urban bridge scenario at 5.9 GHz. The measurement area was selected in Wuhan city, which lies on the middle reaches of the Yangtze River's intersection. Due to urban bridges, the fading characteristics of inland waterway channels are highly correlated with the ship motion direction or the distance between the transmitter and receiver and thus have unique properties. We demonstrated that the path loss, K-factor, power delay profile characteristics, and delay spread features significantly varied with the distance between the transmitter and receiver. Path-loss exponents were derived from the measurements and the differences between the Urban Bridge Environment and the line-of-sight was found. In bridge environments, the values of the excess delays change weakly from line-of-sight cases. The study also showed that numerical measurement results can be used to predict small-scale characteristics over any inland waterway with relatively good accuracy. These results will serve as a reference for urban waterways with bridges, as no experimental results have been reported previously.
Keywords
Urban Bridge Environment, Measurement, Channel Characteristics, Path Loss, Small Scale Fading
To cite this article
Jing Zhang, Changzhen Li, Xuanhao Shu, Wei Chen, Research on Wireless Fading Characteristic in Urban Bridge Environment of the Inland Waterway Based on Channel Measurement, International Journal of Wireless Communications and Mobile Computing. Vol. 7, No. 2, 2019, pp. 38-47. doi: 10.11648/j.wcmc.20190702.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Tao Guo., “Key technology of Changjiang intelligent waterway,” Port&Waterway Engineering, 2016, (01): 99-105.
[2]
QIU Wenqin; TANG Cunbao; TANG Qiangrong., “Navigation Environment Risk Assessment of Uncertain Inland Waterway,” Navigation of China, 2019, (01): P52-55.
[3]
Yu Song MENG, Yee Hui LEE, “Empirical modeling of ducting effects on a mobile microwave link over a sea surface,” Radioengineering, 2012.
[4]
Sumayya Balkees P A, Kalyan Sasidhar, "A Survey based Analysis of Propagation Models Over the Sea, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015.
[5]
QIU Wenqin; TANG Cunbao; TANG Qiangrong., “Status and prospect of technical development for bridges in China,” Chinese Science Bulletin, 2016, V61: P 415-425.
[6]
A. F. Molisch, F. Tufvesson, J. Karedal, and C. Mecklenbrauker, “A survey on vehicle-to-vehicle propagation channels,” IEEE Commun. Lett., vol. 16, no. 6, pp. 12–22, Dec. 2009.
[7]
A. F. Molisch, Wireless Communications, IEEE Press-Wiley, 2005.
[8]
JQ Chen. A non-stationary channel model for 5G massive MIMO systems [J]. Frontiers of Information Technology & Electronic Engineering, 2017. (12): 2101-2111.
[9]
Junyi Yu.. Measurement-Based Characteristics Estimation of Radio Channel for Urban Campus Environment at 5.9 GHz. [J]. The Institution of Engineering and Technology, 2015. 1-9.
[10]
JY. Chen. Method of the Recommendation ITU-R P. 1546-3 for VHF Field-Strength Prediction over sea propagation. Marine Technology, Vol, 3, April 2009, pp. 39–42.
[11]
Reyes-Guerrero, J. C., & Mariscal, L. A. (2012). Experimental time dispersion parameters of wireless channels over sea at 5.8 GHz. In Proceedings of IEEE international symposium on ELMAR, (pp. 89–92).
[12]
Ruisi He, Andreas F. Molisch, Fredrik Tufvesson. Vehicle-to-Vehicle Propagation Models With Large Vehicle Obstructions [J]. IEEE Transactions on Intelligent Transportation Systems, 2014. 15 (5): 2237-2248.
[13]
Song Xin, CQ Yuan. Inluence Analysis of Water Flow Rate on Operating Efficiency of Inland ships. Ship Engineering, Vol. 38 No. 7 2016 P: 54-57.
[14]
P. Bello, “Characterization of randomly time-variant linear channels,”IEEE Trans. Commun. Syst., vol. CS-11, no. 4, pp. 360–393, Dec. 1963.
[15]
Vitaly А. Karbolin, Vladimir I. Nosov. Performance Analysis of Ultra Wide Band Communication System in Terms of Data Rate Dependency and Sampling Rate Dependency of an Indoor Wireless Channel Impulse Response [J]. 14th International Scientific-Technical Conference APEIE, 2018. (): 184-187.
[16]
Taimoor Abbas, Katrin Sjöberg, Johan Karedal, and Fredrik Tufvesson. A Measurement Based Shadow Fading Model for Vehicle-to-Vehicle Network Simulations [J]. International Journal of Antennas and Propagation, 2015. (): 1-12.
[17]
Vlastaras D, Abbas T, Nilsson M, et al. Impact of a truck as an obstacle on vehicle-to-vehicle communications in rural and highway scenarios [C]//IEEE, International Symposium on Wireless Vehicular Communications. IEEE, 2014: 1-6,
[18]
J.-M. Molina-Garcia-Pardo; M. Lienard. Wideband analysis of large scale and small scale fading in tunnels [J]. International Conference on ITS Telecommunications, Thailand, 2009. (5): 1-4.
[19]
H. Akaike, “Information theory and an extension of the maximum likelihood principle,” in Selected Papers of Hirotugu Akaike. Springer, 1998, pp. 199–213.
[20]
Haikuan Feng; Haojie Pei; Fuqin Yang. Estimation of leaf nitrogen content of maize based on Akaike's information criterion in Beijing [J]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. (7): 5057-5060.
[21]
Jie Ding; Vahid Tarokh; Yuhong Yang. Bridging AIC and BIC: A New Criterion for Autoregression [J]. IEEE Transactions on Information Theory, 2018. 64 (6): 4024-4043.
[22]
Y. Zhang and Y. Yang, “Cross-validation for selecting a model selection procedure,” J. Econometrics, vol. 187, no. 1, pp. 95–112, 2015.
[23]
C. A. Guti´errez, M. P atzold, W. Dahech, and N. Youssef, “A non-WSSUS mobile-to-mobile channel model assuming velocity variations of the mobile stations,” in Proc. IEEE Wireless Commun. Netw. Conf., San Francisco, CA, USA, 2017, pp. 1–6.
[24]
W. Dahech, M. P¨atzold, C. A. Guti´errez, and N. Youssef, “A non-stationary mobile-to-mobile channel model allowing for velocity and trajectory variations of the mobile stations,” IEEE Trans. Wireless Commun., vol. 16, no. 3, pp. 1987–2000, Mar. 2017
[25]
L. Bernado, T. Zemen, F. Tufvesson, A. F. Molisch, and C. F. Mecklenbr ¨auker, “Delay and Doppler spreads of nonstationary vehicular channels for safety-relevant scenarios,” IEEE Trans. Veh. Technol., vol. 63, no. 1, pp. 82–93, Jan. 2014.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186