  Compressed sensing has been employed to reduce the pilot overhead for channel
estimation in wireless communication systems. Particularly, structured turbo
compressed sensing (STCS) provides a generic framework for structured sparse
signal recovery with reduced computational complexity and storage requirement.
In this paper, we consider the problem of massive multiple-input
multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)
channel estimation in a frequency division duplexing (FDD) downlink system. By
exploiting the structured sparsity in the angle-frequency domain (AFD) and
angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the
channel by using AFD and ADD probability models and design message-passing
based channel estimators under the STCS framework. Several STCS-based
algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting
the structured sparsity. We show that, compared with other existing algorithms,
the proposed algorithms have a much faster convergence speed and achieve
competitive error performance under a wide range of simulation settings.
