  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.
