Structured Compressive Channel Estimation for Large-Scale MISO-OFDM Systems

To estimate the increased channel parameters in large-scale multiple-input-single-output (MISO) systems, a structured compressive channel estimation scheme based on preamble signals is proposed. The channel estimation scheme exploits the sparse common support of different channel impulse responses (CIRs), leading to a block-structured compressive sensing model for the MISO system. Using this model, an optimization criteria characterizing the unique block sparsity is formulated, and accordingly a block-based orthogonal matching pursuit algorithm is developed which effectively recovers the channel parameters. Simulation results validate the efficacy of the proposed scheme in estimating many sparse CIRs, showing its performance advantage in the emerging large-scale antenna systems.