  Orthogonal time frequency space (OTFS) modulation has the potential to enable
robust communications in highly-mobile scenarios. Estimating the channels for
OTFS systems, however, is associated with high pilot signaling overhead that
scales with the maximum delay and Doppler spreads. This becomes particularly
challenging for massive MIMO systems where the overhead also scales with the
number of antennas. An important observation however is that the delay,
Doppler, and angle of departure/arrival information are directly related to the
distance, velocity, and direction information of the mobile user and the
various scatterers in the environment. With this motivation, we propose to
leverage radar sensing to obtain this information about the mobile users and
scatterers in the environment and leverage it to aid the OTFS channel
estimation in massive MIMO systems.
  As one approach to realize our vision, this paper formulates the OTFS channel
estimation problem in massive MIMO systems as a sparse recovery problem and
utilizes the radar sensing information to determine the support (locations of
the non-zero delay-Doppler taps). The proposed radar sensing aided sparse
recovery algorithm is evaluated based on an accurate 3D ray-tracing framework
with co-existing radar and communication data. The results show that the
developed sensing-aided solution consistently outperforms the standard sparse
recovery algorithms (that do not leverage radar sensing data) and leads to a
significant reduction in the pilot overhead, which highlights a promising
direction for OTFS based massive MIMO systems.
