Accurate channel estimation of on-grid partially coherent compressive phase retrieval for mmWave massive MIMO systems

Abstract Channel estimation of sparse signal is a challenging task for millimeter-wave massive multi-input multi-output systems. Due to the hardware imperfections and use of large carrier frequency oscillator in hybrid precoding architectures leads to random phase drifts in the received pilots. Channel estimation with random phase drifts exploits partial coherence, where pilots share phase distortion within a frame but differ across different frames. To achieve the reliable and efficient communication, the support vector initialization needs to be optimized effectively. In this article, the singular value decomposition-based on-grid partially coherent compressive phase retrieval (SVD-PC-CPR) algorithm is proposed. This algorithm optimizes the initial support vector by finding the best dimensional subspace of the k sparsity elements by forming the singular vectors. The channel vectors are estimated by applying the gradient descent method through iterative refinements. This algorithm utilizes SVD for identifying the dominant paths from the phases of the measurements by selecting basis vectors. Based on the simulation results, the proposed algorithm achieves better normalized mean squared error, a multi-user sum-rate gain of 19 bits/s/Hz, and a 48 % improvement in estimation accuracy over the conventional PC-CPR algorithm at 20 dB signal-to-noise ratio.