Spectrum-efficient superimposed pilot design based on structured compressive sensing for downlink large-scale MIMO systems

Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. Although most research only considers training and channel estimation in the uplink based on the assumption of time division duplexing (TDD) protocol, downlink training and channel estimation is also necessary, especially for the dominated frequency division duplexing (FDD) protocol. Unlike conventional orthogonal pilots whose overhead prohibitively increases with the number of transmit antennas, we propose a spectrum-efficient superimposed pilot design based on the emerging theory of structured compressive sensing, whereby the frequency-domain pilots of different transmit antennas share common subcarriers instead of orthogonal subcarriers. Accordingly, we propose the structured compressive sampling matching pursuit (CoSaMP) algorithm to simultaneously recover multiple channels by exploiting the spatial and temporal correlations of large-scale MIMO channels. Simulation results verify that the proposed scheme can approach the performance bound of the exact least square algorithm.