Joint Sparsity Pattern Learning Based Channel Estimation for Massive
  MIMO-OTFS Systems
Kuo Meng Shaoshi Yang Xiao-Yang Wang Yan Bu Yurong Tang Jianhua Zhang Lajos Hanzo
We propose a channel estimation scheme based on joint sparsity pattern
learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal
time-frequency-space (OTFS) modulation aided systems. By exploiting the
potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the
channel estimation problem is transformed into a sparse recovery problem. To
solve it, we first apply the spike and slab prior model to iteratively estimate
the support set of the channel matrix, and a higher-accuracy parameter update
rule relying on the identified support set is introduced into the iteration.
Then the specific values of the channel elements corresponding to the support
set are estimated by the orthogonal matching pursuit (OMP) method. Both our
simulation results and analysis demonstrate that the proposed JSPL channel
estimation scheme achieves an improved performance over the representative
state-of-the-art baseline schemes, despite its reduced pilot overhead.