Path Loss Model Optimization In An Urban Environment Using Genetic Algorithm

Authors

  • Lee Loo Chuana, Mardeni Rosleea, Chilakala Sudhamania, Athar Waseemb, Anwar Faizd Osmanc, Mohamad Huzaimy Jusohd

Keywords:

Path Loss Model, 5G networks, Optimization, Genetic Algorithm

Abstract

An essential requirement for the design of a wireless communication system is the determination of the path loss. This article compares and estimates path loss using urban macro environment path loss models. Path loss model optimization is taken into consideration to represent the real propagation path and to find the optimized path loss model using a genetic algorithm. The analytically measured path loss is contrasted with the optimized path loss values of each model and error statics are used to assess each model’s performance. From the results, it can be deduced that the 5GCM LOS and NLOS model generates the mean square error and standard deviation with the lowest values. This model allows to improve the accuracy by 90.30%. The 5G heterogeneous network operators can improve the service quality at millimeter wave frequencies by employing 5GCM path loss model.

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References

Oseni, O. F., Popoola, S. I., Enumah, H., & Gordian, A. (2014). Radio frequency optimization of mobile networks in Abeokuta, Nigeria for improved quality of service. International Journal of Research in Engineering and Technology, 3(8), 174-180.

Mohamed, K. S., Alias, M. Y., Roslee, M., & Raji, Y. M. (2021). Towards green communication in 5G systems: Survey on beamforming concept. IET Communications, 15(1), 142-154. https://doi.org/10.1049/cmu2.12066

Abuajwa, O., Roslee, M. B., & Yusoff, Z. B. (2021). Simulated annealing for resource allocation in downlink NOMA systems in 5G networks. Applied Sciences, 11(10), 4592. https://doi.org/10.3390/app11104592

Saleem, A., Zhang, X., Xu, Y., Albalawi, U. A., & Younes, O. S. (2023). A Critical Review on Channel Modeling: Implementations, Challenges and Applications. Electronics, 12(9), 2014. https://doi.org/10.3390/electronics12092014

Ojo, S., Sari, A., & Ojo, T. P. (2022). Path loss modeling: A machine learning based approach using support vector regression and radial basis function models. Open Journal of Applied Sciences, 12(6), 990-1010. 10.4236/ojapps.2022.126068

Roslee, M., Subari, K. S., & Shahdan, I. S. (2011, December). Design of bow tie antenna in CST studio suite below 2GHz for ground penetrating radar applications. In 2011 IEEE International RF & Microwave Conference (pp. 430-433). IEEE.

Zakaria, Y. A., Hamad, E. K., Abd Elhamid, A. S., & El-Khatib, K. M. (2021). Developed channel propagation models and path loss measurements for wireless communication systems using regression analysis techniques. Bulletin of the National Research Centre, 45(1), 1-11. https://doi.org/10.1186/s42269-021-00509-x

Kordi, K. A., Alhammadi, A., Roslee, M., Alias, M. Y., & Abdullah, Q. (2020, November). A review on wireless emerging IoT indoor localization. In 2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT) (pp. 82-87). IEEE. 10.1109/ISTT50966.2020.9279386

Roslee, M. B., & Kwan, K. F. (2010). Optimization of Hata propagation prediction model in suburban area in Malaysia. Progress In Electromagnetics Research C, 13, 91-106. 10.2528/PIERC10011804

lhammadi, A., Roslee, M., Alias, M. Y., Shayea, I., Alraih, S., & Mohamed, K. S. (2019). Auto tuning self-optimization algorithm for mobility management in LTE-A and 5G HetNets. IEEE Access, 8, 294-304. 10.1109/ACCESS.2019.2961186

Bhuvaneshwari, A., Hemalatha, R., & SatyaSavithri, T. (2018). Path loss model optimization using stochastic hybrid genetic algorithm. International Journal of Engineering and Technology (UAE), 7, 464-469.

Bhuvaneshwari, A., Hemalatha, R., & SatyaSavithri, T. (2019). Comparison of Meta-Heuristic Algorithms for Mobile Radio Path Loss model Optimization. International Journal of Advance Computational Engineering and Networking (IJACEN) , 7(9), 42-49.

Bhuvaneshwari, A., Hemalatha, R., and Satyasavithri, T. (2013). Statistical tuning of the best suited prediction model for measurements made in Hyderabad city of Southern India. Proceedings of the world congress on engineering and computer science, 2.

Mehta, R. (2020). Path loss estimation in wireless networks using partial derivative based convex optimisation method. International Journal of Autonomous and Adaptive Communications Systems, 13(3), 229-245. https://doi.org/10.1504/IJAACS.2020.110748

Yang, G., Zhang, Y., He, Z., Wen, J., Ji, Z., & Li, Y. (2019). Machine‐learning‐based prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels. IET Microwaves, Antennas & Propagation, 13(8), 1113-1121. https://doi.org/10.1049/iet-map.2018.6187

Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., ... & Gutierrez, F. (2013). Millimeter wave mobile communications for 5G cellular: It will work!. IEEE access, 1, 335-349. 10.1109/ACCESS.2013.2260813

Alquhali, A. H., Roslee, M., Alias, M. Y., & Mohamed, K. S. (2020). D2D communication for spectral efficiency improvement and interference reduction: A survey. Bulletin of Electrical Engineering and Informatics, 9(3), 1085-1094. https://doi.org/10.11591/eei.v9i3.2171

Sudhamani, C., Roslee, M., Tiang, J. J., & Rehman, A. U. (2023). A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges. Sensors, 23(4), 2356. https://doi.org/10.3390/s23042356

Roy, S., Tiang, J. J., Roslee, M. B., Ahmed, M. T., Kouzani, A. Z., & Mahmud, M. P. (2022). Design of a highly efficient wideband multi-frequency ambient RF energy harvester. Sensors, 22(2), 424.

Jaeckel, S., Peter, M., Sakaguchi, K., Keusgen, W., & Medbo, J. (2016, May). 5G channel models in mm-wave frequency bands. In European Wireless 2016; 22th European Wireless Conference (pp. 1-6).

Rappaport, T. S., Xing, Y., MacCartney, G. R., Molisch, A. F., Mellios, E., & Zhang, J. (2017). Overview of millimeter wave communications for fifth-generation (5G) wireless networks—With a focus on propagation models. IEEE Transactions on antennas and propagation, 65(12), 6213-6230. 10.1109/TAP.2017.2734243

Rappaport, T. S., MacCartney, G. R., Samimi, M. K., & Sun, S. (2015). Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design. IEEE transactions on Communications, 63(9), 3029-3056. 10.1109/TCOMM.2015.2434384

MacCartney Jr, G. R., Sun, S., Rappaport, T. S., Xing, Y., Yan, H., Koka, J., ... & Yu, D. (2016, October). Millimeter wave wireless communications: New results for rural connectivity. In Proceedings of the 5th workshop on all things cellular: operations, applications and challenges (pp. 31-36). https://doi.org/10.1145/2980055.2987353

Thomas, T. A., Rybakowski, M., Sun, S., Rappaport, T. S., Nguyen, H., Kovacs, I. Z., & Rodriguez, I. (2016, May). A prediction study of path loss models from 2-73.5 GHz in an urban-macro environment. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE. 10.1109/VTCSpring.2016.7504094

Haneda, K., Zhang, J., Tan, L., Liu, G., Zheng, Y., Asplund, H., ... & Ghosh, A. (2016, May). 5G 3GPP-like channel models for outdoor urban microcellular and macrocellular environments. In 2016 IEEE 83rd vehicular technology conference (VTC spring) (pp. 1-7). IEEE. 10.1109/VTCSpring.2016.7503971

JA, S. (2005). Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms, 2nd edn. Applied optimization, vol. 97. https://www.jstor.org/stable/20141453

Chilakala, S., & Ram, M. S. S. (2018). Optimization of cooperative secondary users in cognitive radio networks. Engineering science and technology, an international journal, 21(5), 815-821. https://doi.org/10.1016/j.jestch.2018.07.013

Michalewicz, Z. (1999). Genetic Algorithms+ Data Structures= Evolution Programs. Springer-Verlag, 1999. Google Scholar Google Scholar Digital Library Digital Library.

Emmerich, M. T., & Deutz, A. H. (2018). A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural computing, 17, 585-609. https://doi.org/10.1007/s11047-018-9685-y

Li, B., Li, J., Tang, K., & Yao, X. (2015). Many-objective evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 48(1), 1-35. https://doi.org/10.1145/2792984

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Published

16.03.2024

How to Cite

Anwar Faizd Osmanc, Mohamad Huzaimy Jusohd, L. L. C. M. R. C. S. A. W. . (2024). Path Loss Model Optimization In An Urban Environment Using Genetic Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 893–900. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5369

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Section

Research Article