Base Station Selection with 3GPP TR 38,901 Channel Model Based Multiobjective NSGA-II Algorithm

Abstract

The increase in data demand in mobile communication systems, parallel to the increase in the number of users, has led to serious problems encountered in the base station selection processes in 5G and beyond wireless networks. The distance between the base station and the user, and the signal attenuation caused by this distance, lead to poor signal quality and interference. Effective base station selection plays a critical role in improving the overall performance of communication networks. In traditional base station selection, the desired performance cannot be achieved because the maximum Received Signal Strength Indicator (RSSI) cannot optimize interference and load balancing. Based on these problems, this study created a realistic channel model for the 5G system using the 3GPP TR 38.901 channel model and addressed base station selection. This selection process was formulated using a multi-objective optimization method and solved with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The simulation solution was obtained by considering the maximization of signal quality and the minimization of the distance between the base station and the user as the objective functions used in the NSGA-II algorithm. The results obtained showed that the proposed method provides more balanced and efficient solutions and increases the performance rate.

Keywords

• NSGA-II
• Multi-objective optimization
• SINR
• 5G
• Base station selection

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

References

1. 3GPP. (2026). 5G; Study on channel model for frequencies from 0.5 to 100 GHz (3GPP TR 38.901 version 19.2.0 Release 19). ETSI. https://www.etsi.org/deliver/etsi_tr/138900_138999/138901/19.02.00_60/tr_138901v190200p.pdf
2. 3GPP. (2014). 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Study on 3D channel model for LTE (Release 12). https://www.academia.edu/29151372/3rd_Generation_Partnership_Project_Technical_Specification_Group_Radio_Access_Network_Study_on_3D_channel_model_for_LTE_Release_12
3. Ademaj, F., Schwarz, S., Berisha, T., & Rupp, M. (2019). A spatial consistency model for geometry-based stochastic channels. IEEE Access, 7, 183414–183427. https://doi.org/10.1109/ACCESS.2019.2958154
4. Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082. https://doi.org/10.1109/JSAC.2014.2328098
5. Ashtiani, S. H. M., & Martynenko, A. (2025). Nature-inspired approaches for optimizing food drying processes: A critical review. Food Engineering Reviews, 17(2), 270–290. https://doi.org/10.1007/s12393-025-09396-8
6. Celik, H., & Karaboga, N. (2022). Blind Source Separation with Multi-Objective Optimization for Denoising. Elektronika ir Elektrotechnika, 28(5), 62-67. https://doi.org/10.5755/j02.eie.31232
7. Deng, W., Zhang, X., Zhou, Y., Liu, Y., Zhou, X., Chen, H., & Zhao, H. (2022). An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. Information Sciences, 585, 441–453. https://doi.org/10.1016/j.ins.2021.11.052
8. Elechi, P., Orike, S., Okowa, E., & Ogbuji, C. O. (2021). Empirical analysis of global system for mobile communication signal loss in buildings. Journal of Network Security Computer Networks, 7(3), 1–12.

9. Endovitskiy, E., Kureev, A., & Khorov, E. (2022). Reducing computational complexity for the 3GPP TR 38.901 MIMO channel model. IEEE Wireless Communications Letters, 11(6), 1133–1136. https://doi.org/10.1109/LWC.2022.3158095
10. Gao, K., Wang, H., Lv, H., & Liu, W. (2024). Localization-oriented digital twinning in 6G: A new indoor-positioning paradigm and proof-of-concept. IEEE Transactions on Wireless Communications, 23(8), 10473–10486. https://doi.org/10.1109/TWC.2024.3373034
11. Garah, M., Djouane, L., Oudira, H., & Hamdiken, N. (2016). Path loss models optimization for mobile communication in different areas. Indonesian Journal of Electrical Engineering and Computer Science, 3(1), 126–135.
12. Hervis Santana, Y., Martinez Alonso, R., Guillen Nieto, G., Martens, L., Joseph, W., & Plets, D. (2022). Indoor genetic algorithm-based 5G network planning using a machine learning model for path loss estimation. Applied Sciences, 12(8), Article 3923. https://doi.org/10.3390/app12083923
13. Houssein, E. H., Saeed, M. K., Hu, G., & Al-Sayed, M. M. (2024). Metaheuristics for solving global and engineering optimization problems: Review, applications, open issues and challenges. Archives of Computational Methods in Engineering, 31(8), 4485–4519. https://doi.org/10.1007/s11831-024-10168-6
14. Jaeckel, S., Raschkowski, L., Burkhardt, F., & Thiele, L. (2018). Efficient sum-of-sinusoids-based spatial consistency for the 3GPP new-radio channel model. In 2018 IEEE Globecom Workshops (GC Wkshps) (pp. 1-7). IEEE.
15. https://doi.org/10.1109/GLOCOMW.2018.8644265
16. Kakkar, D., Singh, R., & Sebastian, J. (2022). Wireless propagation models optimization using artificial neural network algorithm. In 2022 International Conference for Advancement in Technology (ICONAT) (pp. 1-5). IEEE. https://doi.org/10.1109/ICONAT53423.2022.9725844
17. Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
18. Kelly, M. G. (2016). The automatic placement of multiple indoor antennas using Particle Swarm Optimisation (Doctoral dissertation, Loughborough University). Institutional Repository. https://hdl.handle.net/2134/25434
19. Lalwani, P., Banka, H., & Kumar, C. (2018). BERA: A biogeography-based energy saving routing architecture for wireless sensor networks. Soft Computing, 22(5), 1651–1667. https://doi.org/10.1007/s00500-016-2429-y
20. Lee, S., Lee, S., Kim, K., & Kim, Y. H. (2015). Base station placement algorithm for large-scale LTE heterogeneous networks. PLoS ONE, 10(10), Article e0139190. https://doi.org/10.1371/journal.pone.0139190
21. Mirjalili, S. (2018). Genetic algorithm. In Evolutionary algorithms and neural networks: Theory and applications (pp. 43–55). Springer International Publishing.
22. https://doi.org/10.1007/978-3-319-93025-1_4
23. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2013). Survey of multiobjective evolutionary algorithms for data mining: Part II. IEEE Transactions on Evolutionary Computation, 18(1), 20–35. https://doi.org/10.1109/TEVC.2013.2290082
24. Nwelih, E., Isabona, J., & Imoize, A. L. (2022). Optimization of base station placement in 4G LTE broadband networks using adaptive variable length genetic algorithm. SN Computer Science, 4(2), Article 121. https://doi.org/10.1007/s42979-022-01533-y
25. Poddar, H., Gold, D., Lee, D., Zhang, N., Sridharan, G., Asplund, H., & Shafi, M. (2025a). Overview of 3GPP release 19 study on channel modeling enhancements to TR 38.901 for 6G. arXiv preprint arXiv:2507.19266. https://doi.org/10.48550/arXiv.2507.19266
26. Poddar, H., Yoshimura, T., & Ishii, A. (2025b). Validation of 3GPP TR 38.901 Indoor Hotspot Path Loss Model Based on Measurements Conducted at 6.75, 16.95, 28, and 73 GHz for 6G and Beyond. In 2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring) (pp. 1-6). IEEE. https://doi.org/10.1109/VTC2025-Spring65109.2025.11174821
27. Rao, P. S., Lalwani, P., Banka, H., & Rao, G. S. N. (2021). Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks. Multimedia Tools and Applications, 80(17), 26093–26119. https://doi.org/10.1007/s11042-021-10901-4
28. Rappaport, T. S. (2024). Wireless communications: Principles and practice (2nd ed.). Cambridge University Press.
29. Semwal, V. B., Lalwani, P., Mishra, M. K., Bijalwan, V., & Chadha, J. S. (2021). An optimized feature selection using bio-geography optimization technique for human walking activities recognition. Computing, 103(12), 2893–2914. https://doi.org/10.1007/s00607-021-01008-7
30. Sibe, R. T., BO, O., & Biebuma, J. J. Geographic Information System (GIS)-Based Path Loss Model for Southern Nigeria.
31. Sun, S., Rappaport, T. S., Shafi, M., Tang, P., Zhang, J., & Smith, P. J. (2018). Propagation models and performance evaluation for 5G millimeter-wave bands. IEEE Transactions on Vehicular Technology, 67(9), 8422–8439. https://doi.org/10.1109/TVT.2018.2848208
32. Svendsen, S., Gold, D., Rom, C., Pauli, V., & Nurmela, V. (2026). Realistic UE Antennas for 6G in the 3GPP Channel Model. IEEE Communications Standards Magazine. https://doi.org/10.1109/MCOMSTD.2026.3658189
33. Taufique, A., Jaber, M., Imran, A., Dawy, Z., & Yacoub, E. (2017). Planning wireless cellular networks of future: Outlook, challenges and opportunities. IEEE Access, 5, 4821–4845.
34. https://doi.org/10.1109/ACCESS.2017.2680318
35. Ting, C. K., Lee, C. N., Chang, H. C., & Wu, J. S. (2009). Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(4), 945-958. https://doi.org/10.1109/TSMCB.2008.2010951
36. Tse, D., & Viswanath, P. (2005). Fundamentals of wireless communication. Cambridge University Press.
37. Verma, S., Pant, M., & Snasel, V. (2021). A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems. IEEE Access, 9, 57757–57791. https://doi.org/10.1109/ACCESS.2021.3070634
38. Zhu, Q., Wang, C. X., Hua, B., Mao, K., Jiang, S., & Yao, M. (2021). 3GPP TR 38.901 channel model. In the wiley 5G Ref: the essential 5G reference online (pp. 1-35). Wiley Press.