  Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G
wireless communication systems owing to its spectrum and energy efficiency.
However, hundreds of antennas require large volumes of pilot overhead to
guarantee reliable channel estimation in FDD massive MIMO system. Compressive
sensing (CS) has been applied for channel estimation by exploiting the inherent
sparse structure of massive MIMO channel but suffer from high complexity. To
overcome this challenge, this paper develops a hybrid channel estimation scheme
by integrating the model-driven CS and data-driven deep unrolling technique.
The proposed scheme consists of a coarse estimation part and a fine correction
part to respectively exploit the inter- and intraframe sparsities of channels
to greatly reduce the pilot overhead. Theoretical result is provided to
indicate the convergence of the fine correction and coarse estimation net.
Simulation results are provided to verify that our scheme can estimate MIMO
channels with low pilot overhead while guaranteeing estimation accuracy with
relatively low complexity.
