Location-Aware Beamforming in 5G mmWave UDN. Part 2. Spatial Selection by Beam Width Control

Ongoing densification of gNodeB (gNB) and User Equipment (UE) transceivers in 5G and B5G (Beyond 5G) UltraDense Networks (UDN) yields utilization of narrow pencil beams highly relevant. Proper beamforming (BF) at the transmitter and/or receiver requires obtaining accurate Direction of Departure (DOD...

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Bibliographic Details
Published inSystems of Signals Generating and Processing in the Field of on Board Communications (Online) pp. 1 - 6
Main Authors Volgushev, Dmitriy, Fokin, Grigoriy
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.03.2024
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ISSN2768-0118
DOI10.1109/IEEECONF60226.2024.10496764

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Summary:Ongoing densification of gNodeB (gNB) and User Equipment (UE) transceivers in 5G and B5G (Beyond 5G) UltraDense Networks (UDN) yields utilization of narrow pencil beams highly relevant. Proper beamforming (BF) at the transmitter and/or receiver requires obtaining accurate Direction of Departure (DOD) and/or Direction of Arrival (DOA) angles in each gNB-UE radio link. Instead of preliminary acquisition of channel state information (CSI), characterized by huge overhead in highly mobile scenarios, Location-Aware Beamforming (LAB) seems to be a more promising approach for emerging compact massive MIMO systems in millimeter waves (mmWave). The contribution of current research is quantitative establishment of the potential for spatial selection of useful Signal-Of-Interest (SOI) by Half Power Beam Width (HPBW) control. The novelty of the proposed research is implementation of \text{LAB} at \text{gNB} with beam width and orientation adaptation according to location accuracy, characterized by Root Mean Square Error (RMSE) of the UE coordinate estimate. Simulation results revealed, that antenna radiation pattern (ARP) beam width control is most effective with a number of antenna elements more than 15 in one dimension for Uniform Rectangular Array (URA) and RMSE more than 5{\text{\;m}} . Matlab source code is available at https://github.com/grihafokin/LAB_spatial_selection.
ISSN:2768-0118
DOI:10.1109/IEEECONF60226.2024.10496764