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

Sensing Aided OTFS Channel Estimation for Massive MIMO Systems