An Improved QMDOMP Channel Estimation Algorithm in mmWave Massive MIMO Systems

Channel estimation is critical and challenging for millimeter-wave (mmWave) massive multiple input multiple output (MIMO) systems. In this paper, the quantum mechanics denoising (QMD)-based algorithm and the orthogonal matching pursuit (OMP)-based algorithm are combined for channel estimation. The proposed QMDOMP-based algorithm has two parts. The first part is to denoise the received signal by using the QMD-based algorithm. Firstly, according to the received signal, the Hamiltonian matrix is constructed and the corresponding eigenvalues and eigenvectors are calculated. Then, the coefficients are calculated by projecting the received signal onto the adaptive basis. Using the soft threshold processing of coefficients, the denoised received signal is reconstructed. The second part is that the denoised received signal uses the OMP-based algorithm for channel estimation. In each iteration, the correlation between the sensing matrix and the residual signal is obtained. Then, the greatest correlation column of the sensing matrix is selected to update the residual. After several iterations, the selected atoms form a support set. Simulation results show that the proposed QMDOMP-based algorithm shows better estimation accuracy.