An Optimized Path Loss Model for Urban Wireless Channels

  • Sreevardhan CheerlaEmail author
  • D. Venkata Ratnam
  • J. R. K. Kumar Dabbakuti
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 655)


The mobile network planner relies on a signal propagation path loss model to enhance the wireless communication system in order to avail an acceptable limit of quality of service for the mobile users. Hence, it is very crucial to find a robust propagation model suitable for a range of environmental conditions which may be implemented as guidelines for planning of cell in wireless communication systems. Path loss is the regulating factor in limiting the performance of the system in urban areas. It is essential to develop an appropriate path loss model which predicts the path loss values depending on the received signal strength. In the present paper, the COST 231 propagation model has been optimized by making use of Newton’s method. The statistical measures like absolute average error and root-mean-square error were calculated for the frequencies 800 and 1800 MHz. From the simulation results, it is found that the optimized model best acclimatizes with a smaller mean relative error. The lesser value of mean error supports successful implementation of the optimization technique and therefore suggested that the present optimized model can be useful for telecommunication providers to improve the service for mobile user satisfaction.


Path loss GSM CWI model Optimization Newton’s method 



This work was supported by the Department of Science and Technology (DST), New Delhi, India, SR/FST/ESI-130/2013(C), under DST-FIST.


  1. 1.
    Alqudah YA (2013) On the performance of Cost 231 Walfisch Ikegami model in deployed 3.5 GHz network. In: 2013 international conference on technological advances in electrical, electronics and computer engineering (TAEECE). IEEE, pp 524–527Google Scholar
  2. 2.
    Lopez-Barrantes AJ, Gutierrez O, Saez de Adana F, Kronberger R (2012) Comparison of empirical models and deterministic models for the analysis of interference in indoor environments. In: 2012 Asia-Pacific symposium on electromagnetic compatibility (APEMC). IEEE, pp 509–512Google Scholar
  3. 3.
    Seidel SY, Rappaport TS (1992) 914 MHz path loss prediction models for indoor wireless communications in multifloored buildings. IEEE Trans Antennas Propag 40(2):207–217CrossRefGoogle Scholar
  4. 4.
    Rappaport TS, Seidel SY, Schaubach KR (1993) Site-specific propagation prediction for PCS system design. In: Wireless personal communications. Springer, Boston, MA, pp 281–315Google Scholar
  5. 5.
    Tsang K-F, Chan W-S, Jing D, Kang K, Yuen S-Y, Zhang W-X (1998) Radiosity method: a new propagation model for microcellular communication. In: IEEE antennas and propagation society international symposium, 1998, vol 4. IEEE, pp 2228–2231Google Scholar
  6. 6.
    Hrovat A, Kandus G, Javornik T (2014) A survey of radio propagation modeling for tunnels. IEEE Commun Surv Tutor 16(2):658–669CrossRefGoogle Scholar
  7. 7.
    Tan SY, Tan HS (1996) A microcellular communications propagation model based on the uniform theory of diffraction and multiple image theory. IEEE Trans Antennas Propag 44(10):1317–1326CrossRefGoogle Scholar
  8. 8.
    Popescu I, Nafornita I, Constantinou P (2005) Comparison of neural network models for path loss prediction. In: IEEE international conference on wireless and mobile computing, networking and communications, 2005 (WiMob’2005), vol 1. IEEE, pp 44–49Google Scholar
  9. 9.
    Wang Y, Jiang T (2016) Norm adaption penalized least mean square/fourth algorithm for sparse channel estimation. Sig Process 128:243–251CrossRefGoogle Scholar
  10. 10.
    Alotaibi FD, Abdennour A, Ali AA (2008) A robust prediction model using ANFIS based on recent TETRA outdoor RF measurements conducted in Riyadh city–Saudi Arabia. AEU-Int J Electron Commun 62(9):674–682CrossRefGoogle Scholar
  11. 11.
    Sotiroudis SP, Siakavara K (2015) Mobile radio propagation path loss prediction using artificial neural networks with optimal input information for urban environments. AEU-Int J Electron Commun 69(10):1453–1463CrossRefGoogle Scholar
  12. 12.
    Cheerla S, Venkata Ratnam D, Borra HS (2018) Neural network-based path loss model for cellular mobile networks at 800 and 1800 MHz bands. AEU-Int J Electron Commun 94:179–186CrossRefGoogle Scholar
  13. 13.
    Mollel MS, Kisangiri M (2014) Optimization of Hata model based on measurements data using least square method: a case study in Dar-es-Salaam—Tanzania. Int J Comput Appl 102(4)Google Scholar
  14. 14.
    Tahat A, Taha M (2012) Statistical tuning of Walfisch-Ikegami propagation model using particle swarm optimization. In: 2012 IEEE 19th symposium on communications and vehicular technology in the Benelux (SCVT). IEEEGoogle Scholar
  15. 15.
    Roslee MB, Kwan KF (2010) Optimization of Hata propagation prediction model in suburban area in Malaysia. Prog Electromagn Res 13:91–106Google Scholar
  16. 16.
    Munir H, Hassan SA, Pervaiz H, Ni Q, Musavian L (2017) Resource optimization in multi-tier HetNets exploiting multi-slope path loss model. IEEE Access 5:8714–8726Google Scholar
  17. 17.
    Zhu J, Zhao M, Zhou S (2018) An optimization design of ultra dense networks balancing mobility and densification. IEEE Access 6Google Scholar
  18. 18.
    Damosso E, Correia LM (1999) COST action 231: digital mobile radio towards future generation systems: final report. European CommissionGoogle Scholar
  19. 19.
    Alqudah YA (2013) On the performance of COST 231 Walfisch Ikegami model in deployed 3.5 GHz network. IEEE. ISBN: 978-1-4673-5613-8Google Scholar
  20. 20.
    Walfisch J, Bertoni HL (1988) A theoretical model of UHF propagation in urban environments. IEEE Trans Antennas Propag 36(12):1788–1796CrossRefGoogle Scholar
  21. 21.
    Xia HH, Bertoni HL (1992) Diffraction of cylindrical and plane waves by an array of absorbing half-screens. IEEE Trans Antennas Propag 40(2):170–177CrossRefGoogle Scholar
  22. 22.
    Maciel LR, Bertoni HL, Xia HN (1993) Unified approach to prediction of propagation over buildings for all ranges of base station antenna heightGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Sreevardhan Cheerla
    • 1
    Email author
  • D. Venkata Ratnam
    • 1
  • J. R. K. Kumar Dabbakuti
    • 1
  1. 1.Department of Electronics and Communication EngineeringKL Deemed to be University, Koneru Lakshmaiah Education FoundationVaddeswaram, GunturIndia

Personalised recommendations