Weighted Subspace Fitting for Two-Dimension DOA Estimation in Massive MIMO Systems

Massive multiple-input multiple-output system can enable multi-stream transmission for high spectrum efficiency, which is a key technology in future 5G cellular networks. In this paper, we degenerate a direction of arrival (DOA) related 2-D weighted subspace fitting function into two independent parameterized 1-D versions. Based on this, we develop a novel 2-D DOA estimation algorithm, which can be utilized to assist in performing downlink precoding. Furthermore, we also make an analysis on the computational complexity and the theoretical Cramér–Rao lower bound. The direct merits are as follows: the proposed algorithm includes only once polynomial rooting and also does not require angle paring, hence it is of computational efficiency; in addition, compared with some existing algorithms, it can achieve higher 2-D angle estimating accuracy. A series of Monte Carlo simulations are subsequently carried out, which demonstrate the effectiveness of the proposed algorithm.