skip to main content

Studi komparatif empat model propagasi empiris dalam ruangan untuk jaringan nirkabel kampus

Comparative study of four indoor empirical propagation models for campus wireless network

Department of Informatics, STMIK Asia Malang, Indonesia

Received: 6 May 2019; Revised: 11 Sep 2019; Accepted: 18 Sep 2019; Available online: 3 Oct 2019; Published: 31 Oct 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Propagation is one of the important factors to understand in wireless communication systems. Prediction of the value of propagation, especially for closed areas, is very necessary to determine success in building wireless networks. Various kinds of propagation modeling were developed to find the best approach to calculate the value of signal losses. A comparative study of 4 types of empirical propagation modeling was made to provide the most suitable propagation modeling analysis for campus wireless networks. The ITU-R model (P.1238) provides predictive results that are closest to the actual data in the field, with a relative error rate of 16.381%.
Keywords: propagation; wireless network; indoor; campus network
Funding: STMIK Asia Malang, Indonesia

Article Metrics:

  1. M. Morocho-yaguana, P. Lude, F. Sandoval, B. Poma-velez, and A. Erreyes-dota, “An optimized propagation model based on measurement data for indoor environments,” Journal of Telecommunication and Information Technology, vol. 2, pp. 69–75, 2018. doi: 10.26636/jtit.2018.117217
  2. J. C. Stein, “Indoor radio WLAN performance part II : range performance in a dense office environment,” Intersil Corporation, 1998
  3. M. Luo, “Indoor radio propagation modeling for system performance prediction,” thesis, INSA de Lyon, France, 2013
  4. F. Agren, “Indoor radio propagation modelling with antenna placement optimization,” thesis, Lund University, Sweden, 2017
  5. P. Thu, Z. Tun, and A. S. Hlaing, “Modification of propagation prediction model for 2.4 GHz indoor wireless environment,” in International Conference on Advances in Engineering and Technology, Singapore, Mar. 2014, pp. 360–364. doi: 10.15242/IIE.E0314173
  6. S. Y. Yeong, W. Al-Salihy, and T. C. Wan, “Indoor WLAN monitoring and planning using empirical and theoretical propagation models,” in the 2nd International Conference on Network Applications, Protocols and Services, Kedah, Malaysia, Sept. 2010, pp. 165–169. doi: 10.1109/NETAPPS.2010.36
  7. M. O. Omae, E. N. Ndungu, P. L. Kibet, and H. Tarus, “Artificial intelligence approach to signal propagation modeling for outdoor to indoor wireless communication networks ; a proposed study,” in the Mechanical Engineering Conference on Sustainable Research and Innovation, Nairobi, Kenya, May 2012, pp. 289-299
  8. F. J. Carlos Vesga, H. Martha Fabiola Contreras, and B. Jose Antonio Vesga, “Design of empirical propagation models supported in the log-normal shadowing model for the 2.4GHz and 5GHz bands under indoor environments,” Indian Journal of Science & Technology, vol. 11, no. 22, pp. 1-18, 2018. doi: 10.17485/ijst/2018/v11i22/122149
  9. S. Zvanovec, P. Pechac, and M. Klepal, “Wireless LAN networks design: site survey or propagation modeling?,” Radioengineering, vol. 12, no. 4, pp. 42-49, 2003
  10. F. S. Mukti and D. A. Sulistyo, “Analisis penempatan access point pada jaringan wireless lan stmik asia malang menggunakan one slope model,” Jurnal Ilmiah Teknologi Informasi Asia, vol. 13, no. 1, pp. 13-22, 2018
  11. B. R. Jadhavar and T. R. Sontakke, “2.4 GHz propagation prediction models for indoor wireless communications within building,” International Journal of Soft Computing and Engineering, vol. 2, no. 3, pp. 108-113, 2012
  12. A. R. Sandeep, Y. Shreyas, S. Seth, R. Agarwal, and G. Sadashivappa, “Wireless network visualization and indoor empirical propagation model for a campus Wi-fi network,” World Academy of Science, Engineering and Technology, vol. 18, pp. 730-734, 2009
  13. D. Akin et al., Certified wireless network administrator official study guide. Bremen Georgia: Planet3 Wireless Inc., 2002
  14. M. A. Amanaf, E. S. Nugraha, and D. Kurnianto, “Analisis simulasi model cost-231 multiwall pathloss indoor berbasis wireless sensor network pada aplikasi absensi mahasiswa dengan tag RFID menggunakan RPS (radiowave propagation simulator),” Buletin Pos dan Telekomunikasi, vol. 16, no. 1, pp. 17-26, 2018. doi: 10.17933/bpostel.2018.160102
  15. Propagation data and prediction methods for the planning of indoor radio communication systems and the radio local area networks in the frequency range 900 MHz to 100 GHz, ITU-R Recommendations, Geneva, 2001
  16. J. Butler et al., Wireless networking in the developing world, 3rd Edition. Copenhagen: BbookSprint, 2013
  17. L. TP-Link Technologies Co, “Access point TL-WA701ND specification,” 2019. [Online]. Available: https://www.tp-link.com/id/home-networking/access-point/tl-wa701nd/#specifications
  18. L. TP-Link Technologies Co, “Access point Archer C7 specification,” 2019
  19. Ubiquiti Networks, “UniFi dataSheet,” Ubiquiti Nteworks, Inc., 2016
  20. Y. Chapre, P. Mohapatra, S. Jha, and A. Seneviratne, “Received signal strength indicator and its analysis in a typical WLAN system,” in IEEE Conference on Local Computer Networks, Sidney, Australia, Oct. 2013, pp. 304-307. doi: 10.1109/LCN.2013.6761255

Last update:

No citation recorded.

Last update: 2024-11-21 15:31:08

  1. Integrating Cost-231 Multiwall Propagation and Adaptive Data Rate Method for Access Point Placement Recommendation

    Mukti F.S.. International Journal of Advanced Computer Science and Applications, 12 (4), 2021. doi: 10.14569/IJACSA.2021.0120494