The Nesterov Momentum Algorithm for the 5G NR Indoor Scenario Coverage Optimization

• Published in: International Conference on Actual Problems of Electronic Instrument Engineering proceedings pp. 720 - 723
• Main Authors: Akhpashev, Ruslan V., Ozerov, Danil A., Kochkin, Ivan D., Bolotov, Egor D., Yushchenko, Andrey V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.11.2023
• Subjects: coverage planning; Interference; machine learning; Machine learning algorithms; Nesterov momentum; New Radio; optimization; Planning; Software; Software algorithms; Urban areas
• ISSN: 2473-8573
• DOI: 10.1109/APEIE59731.2023.10347881

The 5G New Radio actively growing and it is one of the most evolving areas. It improves people's lives not only in regular life but in business. It provides: seamless connection to entire world, connection for industry, vehicle devices, wearable for sport and health, medicine, etc. The radio signal attenuation prediction also known as a part of radio coverage planning process take an important part in mobile network deployment. It is especially crucial for high carrier frequencies. The 3GPP (3 rd Generation Partnership Project) technical specification 38.901 introduces the 0.5 GHz - 100 GHz usage in 5G network. The problem is to predict radio signal behavior in Urban, Dense Urban areas with high traffic demands. The authors have aimed in Indoor Office scenarios in order to decrease the SINR (Signal to Interference and Noise Ratio) by optimizing the transmission power using Nesterov Momentum algorithm. It is frequently used in machine learning applications. The simulation software is developed, and results show vast coverage (by SINR) improvements.