Simulating of Indoor WIFI Wave Propagation to Select Receiver Localization
Subject Areas : Electronics EngineeringHamidreza Ataii 1 , Mojdeh Mahdavi 2 , Mohsen Maadani 3
1 - - Department of Electronics, Shahr-e-Qods Branch,
Islamic Azad University, Tehran, Iran.
2 - Department of Electronics, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran .
3 - Department of Electronics, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran .
Keywords: CST Software, Ray Tracing, Wave propagation, WIFI,
Abstract :
With the widespread use of WIFI networks, access location optimization is needed to improve network performance in the buildings. Wave propagation inside the building is one of the most challenging issues in network design and optimal location. Different parts of the building have different effects on the propagation of waves inside the building, such as fading, scattering and propagation of waves in several directions. Due to the nature of electromagnetic waves, the metal parts of the building have a great effect on the propagation of waves. In this paper, the effect of different metal parts of the building, including beams and door and window frames in standard dimensions, on the propagation of WIFI waves using Ray Tracing algorithm and CST software is modeled, and propagation patterns for different angles and distances are obtained. With the using of CST software, various modes such as rooms with different dimensions can be modeled. Then, based on the results, calculate the best location for the receiver. The results show that this method provides better capabilities than statistical methods to determine the best location of the receiver in the building.
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_||_[1] G. Miao, J. Zander, K. Won Sung and B. Slimane, “Fundamentals of Mobile Data Networks.” Cambridge University Press, ISBN 1107143217, 2016.
[2] N. Sudhakar Reddy, K. Siddappa Naidu, S. Ashok Kumar, “Performance and design of antenna for UWB band applications.”Alexandria Engineering Journal, vol.57, no. 2, pp. 67, 2018.
[3] S. Xia, Y. Liu, G. Yuan, M. Zhu and Z. Wang. “Indoor Fingerprint Positioning Based on Wi-Fi.” International Journal of Geo-Information, vol.6, no.5, pp. 135, 2017.
[4] A. V. Gureev, Y. I. Shtern, M. Y. Shtern Thurain Tun and I. S. Karavaev. “Mathematical Simulation of Indoor Wireless Networks.” Global Journal of Pure and Applied Mathematics. Vol.12, no. 5, pp. 4001, 2016.
[5]Y. Wang, C. Xiu, X. Zhang, and D. Yang. “WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest.” Sensors, vol.18, no.9. 2018.
[6]A.F. Agelet, A. Formella, J.M.H. Rabanos, I.F. de Vicente, and P.F. Fontan. “CST ray-tracing techniques for radio propagation modeling.” IEEE Veh. Technol. Conf., 2000, pp. 2089–2104.
[7]V.Garmash, S.Matveev, Y.Petrov, V.Rogozhin and S.Rudika, “Processing of radar images containing objects with significantly different radar cross-section in the onboard remote sensing complex for search and rescue operations in the Arctic Region.” ITM Web Conf., 2019, pp.15023.
[8] Y. Zhengqing and I. Magdy. “Ray Tracing for Radio Propagation Modeling: Principles and Applications. Access.” IEEE transaction Principles and Applications, vol.3, pp.1089, 2015.
[9] S. Ahsan, A. Zeeshan and A. Iftikhar, “Analysis and Measurement of WIFI Signals in Indoor Environment.” International journal of Advances in Engineering & Technology, vol.6, no 2. pp 678, 2014.
[10] A. V. Gureev, “Abstraction in Simulation of Indoor Wireless Networks.” Global Journal of Pure and Applied Mathematics vol.12, no. 5, 2016.
[11] A. Alhamoud, M. Kreger, “Empirical investigation of the effect of the door's state on received signal strength in indoor environments at 2.4 GHz.” in IEEE Conference on Local Computer Networks Workshops (LCN Workshops), 2014, pp. 652-657.
[12] A.R Sandeep, Y.Sheryas. “Wireless Network Visualization and Indoor Empirical Propagation Model for a Campus WIFI Network.” IEEE Antennas and Propagation Society International Symposium, vol.2, pp. 150–153, 2014.
[13] R. Mardeni, Y. Solahuddin. “Path loss model development for indoor signal loss prediction at 2.4 GHz” Microwave and Millimeter Wave Technology (ICMMT), International Conference, 2012, pp1-4.
[14] T.T. Khanh, V. Nguyen, XQ. Pham et al. “Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach.” Hum. Cent. Comput. Inf. Sci. vol.10, no.32, 2020.
[15] S. Garcia-Villalonga and A. Perez-Navarro, “Influence of human absorption of Wi-Fi signal in indoor positioning with Wi-Fi fingerprinting,” International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-10, 2015.
[16] K. Ullah, I. V. Custodio, N. Shah and E. D. S. Moreira, “An Experimental Study on the Behavior of Received Signal Strength in Indoor Environment,” in International Conference on Frontiers of Information Technology, pp. 259-264, 2013.
[17] L. Zhang, S. Valaee, Y. Xu, L. Ma and, F. Vedadi “Graph-based semisupervised learning for indoor localizationusing crowdsourced data.” Appl. Sci. vol.7, pp. 467, 2017.
[18] Z. Zhang, Z. Tian, M. Zhou, Z. Li, Z. Wu and Y. Jin, “WIPP: Wi-Fi compass for indoor passive positioning with decimeter accuracy.” Appl. Sci. vol.6, pp. 108, 2016.
[19] N. Hernández, M. Ocaña, J. Alonso and E. Kim, “Continuous space estimation: Increasing WiFi-based indoor localization resolution without increasing the site-survey effort.” Sensors, vol.17,no.1, pp. 147, 2017.
[20] L. Zheng, B. Hu, H. A, Chen,” high accuracy time-reversal based WiFi indoor localization approach with asingle antenna” Sensors, vol.18, no.10, pp. 3437, 2018.
[21] C. Zhenghua, H. Zou, J. Yang, H. Jiang, and Lihua Xie. “WiFi fingerprinting indoor localization using local feature-based deep LSTM.” IEEE Systems Journal, vol.14, no. 2, pp.3001-3010, 2019.
[22] Y. S. Chen, F. P. Lai and J. W. You, “Analysis of Antenna Radiation Characteristics Using a Hybrid Ray Tracing Algorithm for Indoor WiFi Energy-Harvesting Rectennas,” in IEEE Access ,vol.7, pp. 38833-38846, 2019.
