Two-Dimensional Localization: Low-Rank Matrix Completion With Random Sampling in Massive MIMO System
Qi Liu, Xiao Peng Li, Hui Cao
Abstract
In this paper, random sampling is considered for direction-of-arrival (DOA) estimation with reduced hardware complexity in massive multiple-input-multiple-output (MIMO) systems. The resulting problem is that the accuracy of the existing DOA estimators will significantly degrade with the availability of only a small subset of data entries as the low date rate is employed to reduce the system power consumption. To address that, an efficient approach based on the variant of matrix factorization is devised to complete the underlying data matrix. As a result, the nominal azimuth and elevation DOAs with the corresponding angular spreads are estimated from the underlying data matrix. Numerical results demonstrate that even in the case of missing entries, the proposed method is superior to the existing approaches with full data.