A Gridless CS Approach for Channel Estimation in Hybrid Massive MIMO Systems

Channel state information (CSI) estimation in hybrid analog-digital (HAD) millimeter-wave (mmWave) massive MIMO systems is a challenging problem due to the high channel dimension and reduced number of radio-frequency chains. However, exploiting the channel sparsity, several methods have been proposed leveraging the compressed sensing (CS) tools. Most of the prior works consider an approximate CS formulation by assuming that the channel parameters lie perfectly on a finite grid neglecting the grid mismatch effect. To resolve this issue, we propose a gridless CS approach that exploits the antenna array geometry. The proposed algorithm is based on an alternating optimization technique and is guaranteed to converge to a local minimum. Simulation results are provided to evaluate the effectiveness of the proposed algorithm.