Low Complexity DOA Estimation Algorithms Based on Propagator for Massive MIMO Systems

Abstract

Massive multiple-input multiple-output (MIMO) has become one of the primary technologies for wireless communication systems, in which accurate direction of arrival (DOA) estimation plays a critical role in interference cancellation and effective beamforming. However, it is a major challenge to develop DOA estimation algorithms with low-complexity and high-accuracy for massive MIMO systems. To address this issue, we propose two successive propagator method (PM) based DOA estimation algorithms for massive MIMO systems. The proposed computationally efficient modified PM algorithm utilizes conjugate rearrangement preprocessing on the received signals and orthogonalizes the noise subspace to achieve an accurate estimation under low signal-to-noise ratio and small number of snapshots. Furthermore, to avoid the spectrum-peak search of the PM-based method, we further propose a PM-Estimation of Signal Parameters via Rotational Invariance Techniques (PM-ESPRIT) algorithm, which exploits the rotational invariance of the propagator matrix and closed-form expressions to obtain angle estimations without spectrum-peak search. The PM-ESPRIT algorithm exhibits high accuracy while avoiding a large amount of computation caused by eigenvalue decomposition of the high-dimensional covariance matrix. Simulation results indicate that the proposed algorithms substantially enhance the performance and reduce the computational complexity in massive MIMO systems.