Future cellular networks are intended to have the ability to sense the environment by utilizing reflections of transmitted signals. Multi-dimensional sensing brings along the crucial advantage of being able to resort to multiple domains to resolve targets, enhancing detection capabilities compared to 1D estimation. However, estimating parameters jointly in 5G New Radio (NR) systems poses the challenge of limiting the computational complexity while preserving a high resolution. To that end, we make us of channel state information (CSI) decimation for MUltiple SIgnal Classification (MUSIC)-based joint range-angle of arrival (AoA) estimation. We further introduce multi-peak search routines to achieve additional detection capability improvements. Simulation results with orthogonal frequency-division multiplexing (OFDM) signals show that we attain higher detection probabilities for closely spaced targets than with 1D range-only estimation. Moreover, we demonstrate that for our considered 5G setup, we are able to significantly reduce the required number of computations due to CSI decimation.
@article{arxiv.2104.15132,
title = {A Computationally Efficient 2D MUSIC Approach for 5G and 6G Sensing Networks},
author = {Marcus Henninger and Silvio Mandelli and Maximilian Arnold and Stephan ten Brink},
journal= {arXiv preprint arXiv:2104.15132},
year = {2022}
}
Comments
Longer version (with extended proof) of accepted 2022 IEEE Wireless Communications and Networking Conference (WCNC) conference paper. Added copyright notice and DOI