Abstract
The study of Location Aware Beamforming (LAB) issues, based on User Equipment (UE) positioning in millimeter wave (mmWave) ultra-dense networks (UDN), is devoted to the formalization and software implementation of a complex system-level simulation model. The set of directional radio links, simultaneously operating in a common frequency range, is studied as a set of traffic beams. The purpose of this study is to establish the dependence of Signal to Interference plus Noise Ratio (SINR) and Half Power Beam Width (HPBW) on the uncertainty of the UE location. Developed simulation model is available for verification and for the first time made it possible to establish the interdependence of the UE positioning error factors and the required HPBW of the traffic beam for its service. Simulation results showed that as the positioning error decreases from 10 to 1 m, the required beamwidth narrows to 3°, which makes it possible to increase the SINR to 25 dB. Space Division Multiple Access (SDMA) simulation showed that for 64 spatially multiplexed UEs, as the cell size increases from 20 to 300 m, the SINR increases by approximately 30 dB, subject to a beamwidth constraint of 3°. Unlike similar studies, proposed model account the contribution from interference from simultaneously operating traffic beams within its sector, other sectors of its cell and other cells in the network is shown separately for the first time, which allows to differentiate the origin of interference and use scientifically based beamwidth control for their compensation.
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Acknowledgments
Research and development were performed in The Bonch-Bruevich Saint Petersburg State University of Telecommunications and supported by the Ministry of Science and High Education of the Russian Federation by grant number 075-15-2022-1137.
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Fokin, G. (2024). System-Level Model for SINR and HPBW Evaluation in 5G mmWave UDN with Location-Aware Beamforming. In: Koucheryavy, Y., Aziz, A. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2023 2023. Lecture Notes in Computer Science, vol 14542. Springer, Cham. https://doi.org/10.1007/978-3-031-60994-7_10
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