Indoor 3D localization in emergency scenarios through drone based rapid 5G deployment

Indoor 3D localization is particularly challenging due to poor signal coverage and the complex radio propagation environment. We propose the use of multiple drones to conduct 3D indoor positioning in emergency situations in this paper. We estimate the performance metrics for such a system, when complying with 3GPP rel. 16 NR-positioning specifications. We also propose new designs for the PRS (positioning reference signal) used in the standards, when there are different SCS (sub-carrier spacing) involved. We develop a mapping function based on neural networks to map the multiple SNR (Signal to Noise Ratio) from the drone base stations to the positioning error. The key finding from this work is that the common OTDOA (observed time difference of arrival) based positioning methods alone, on bandwidths and SCS available at sub 6 GHz frequencies, will not be able to meet the very strict accuracy and reliability targets for this use case. Adaptation and/or combination with other 3GPP and non-3GPP positioning methods will be required in this regard.