  Motivated by automotive applications, we consider joint radar sensing and
data communication for a system operating at millimeter wave (mmWave) frequency
bands, where a Base Station (BS) is equipped with a co-located radar receiver
and sends data using the Orthogonal Time Frequency Space (OTFS) modulation
format. We consider two distinct modes of operation. In Discovery mode, a
single common data stream is broadcast over a wide angular sector. The radar
receiver must detect the presence of not yet acquired targets and perform
coarse estimation of their parameters (angle of arrival, range, and velocity).
In Tracking mode, the BS transmits multiple individual data streams to already
acquired users via beamforming, while the radar receiver performs accurate
estimation of the aforementioned parameters. Due to hardware complexity and
power consumption constraints, we consider a hybrid digital-analog architecture
where the number of RF chains and A/D converters is significantly smaller than
the number of antenna array elements. In this case, a direct application of the
conventional MIMO radar approach is not possible. Consequently, we advocate a
beam-space approach where the vector observation at the radar receiver is
obtained through a RF-domain beamforming matrix operating the dimensionality
reduction from antennas to RF chains. Under this setup, we propose a likelihood
function-based scheme to perform joint target detection and parameter
estimation in Discovery, and high-resolution parameter estimation in Tracking
mode, respectively. Our numerical results demonstrate that the proposed
approach is able to reliably detect multiple targets while closely approaching
the Cramer-Rao Lower Bound (CRLB) of the corresponding parameter estimation
problem.
