Theoretical analysis and algorithm design of optimized pilot for downlink channel estimation in massive MIMO systems based on compressed sensing

Abstract

Aiming at the pilot design problem in channel estimation of large-scale multiple input multiple output (MIMO) systems, an adaptive autocorrelation matrix reduction parameter pilot optimization algorithm based on channel reconstruction error rate minimization is proposed under the framework of compression perception theory. Firstly, the system model and orthogonal matching pursuit (OMP) algorithm are introduced. Secondly, for minimizing the channel reconstruction error rate, the relation between the expected value of the correlation decision in each iteration of the OMP algorithm and the reconstruction error rate is analyzed. For the optimal expected value of the correlation decision, the relation between the channel reconstruction error rate and the correlation of the pilot matrix column under the OMP algorithm is derived, and the two criteria of optimizing the pilot matrix are obtained: the pilot matrix column correlation expectation and the variance minimization. Then the method of optimizing the pilot matrix is studied, and the corresponding adaptive autocorrelation matrix reduction parameter pilot matrix optimization algorithm is proposed. In each iteration, whether the average column correlation degree of the matrix to be optimized is reduced is used as a judgment condition. The autocorrelation matrix reduction parameter value is adjusted to make the parameters close to the theoretical optimization. The simulation results show that the proposed method has a better column correlation property and lower channel reconstruction error rate than the pilot matrix obtained, separately, by Gaussian matrix, Elad method and low power average column correlation method.