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Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 4

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

Hüsamettin Çelik(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0002-7662-0674
Pages
1867–1874
Publication Date
2026-07-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Effective base station selection plays a critical role in improving the overall performance of communication networks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:Effective base station selection plays a critical role in improving the overall performance of communication networks.
💡
Conclusion & Impact:The results obtained showed that the proposed method provides more balanced and efficient solutions and increases the performance rate.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:NSGA-II · Multi-objective optimization · SINR

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.

Özet

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.

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Cite This Article

Çelik, H. (2026). Base Station Selection with 3GPP TR 38,901 Channel Model Based Multiobjective NSGA-II Algorithm. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 4, 1867–1874. https://doi.org/10.34248/bsengineering.1951384

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