  Orthogonal time frequency space (OTFS) modulation outperforms orthogonal
frequency division multiplexing (OFDM) in high-mobility scenarios. One
challenge for OTFS massive MIMO is downlink channel estimation due to the large
number of base station antennas. In this paper, we propose a 3D structured
orthogonal matching pursuit algorithm based channel estimation technique to
solve this problem. First, we show that the OTFS MIMO channel exhibits 3D
structured sparsity: normal sparsity along the delay dimension, block sparsity
along the Doppler dimension, and burst sparsity along the angle dimension.
Based on the 3D structured channel sparsity, we then formulate the downlink
channel estimation problem as a sparse signal recovery problem. Simulation
results show that the proposed algorithm can achieve accurate channel state
information with low pilot overhead.
