Multi-BS Spatial Spectrum Fusion for 2-D DOA Estimation and Localization Using UCA in Massive MIMO System

Abstract

In the indoor environment, where global positioning system (GPS) fails, the presence of multipath, non-line-of-sight (NLOS), and dense scatterer yields unreliable position estimation. In the massive multiple-input multiple-output (massive MIMO) system, the sparse channel property is exploited here for user localization as an integral part of the network to reduce the power of user equipment (UE). In this study, a novel spatial spectrum fusion estimation and localization (SSFEAL) algorithm using the uniform circular array (UCA) structure is proposed, which is based on the 2-D direction of arrival (DOA) estimation. The spatial spectrum of the incoherent narrowband uplink pilot signal on each frequency bin received at the distributed massive MIMO base stations (BSs) is used for spectrum fusion. The main contributions of this work are as follows: 1) the channel's sparse characteristics at individual BSs are used to synthesize the covariance matrix of the received signal at the central fusion center and 2) the proposed method has 2-D spatial information measurement fusion to generate informative spatial spectrum followed by grid refinement processing to reduce the localization error to submeter level. It is demonstrated that the proposed method using UCA gives higher resolution estimation performance compared with the rectangular array in the localization accuracy. Comprehensive Monte Carlo simulation and real application test results show the validity and submeter precision in the indoor environment.