Turbo Compressed Channel Sensing for Millimeter Wave Communications with Massive Antenna Arrays and RF Chain Constraints

With the abundant frequency spectrum available at millimeter wave (MMW) frequency bands, MMW communications can easily achieve multi-gigabit wireless transmission. One key challenge for MMW system is the design of an efficient and fast channel estimation algorithm with limited RF chains to facilitate the beamforming and coherent detection, in compensation for the severe propagation attenuation. In this paper, an efficient channel estimation algorithm is developed for MMW systems with massive antenna arrays and RF chain constrains to achieve: i) a significant reduction of the time slot consumption; ii) a great enhancement of the estimation reliability in the low signal-to-noise ratio (SNR) region. By fully exploiting the sparse structure of channel in MMW frequency bands, an efficient training method is designed to achieve the fast and reliable channel estimation based on the compressed sensing (CS) methods. An iterative soft decoding method, termed turbo compressed channel sensing (TCCS), is developed to estimate the channel state information (CSI) by iteratively updating the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner, which refines the estimation by exploiting the sparsity of CSI. Simulation results show that the proposed approach greatly outperforms existing approaches.