Low-Complexity 3D AoA Positioning for 5G RedCap UEs in Multipath Indoor Factory Environments

The problem of Reduced Capability (RedCap) User Equipment (UE) positioning within indoor 5G networks is addressed. While conventional approaches rely on time-domain ranging, the limited signal bandwidth associated with RedCap devices often prevents these methods from satisfying stringent accuracy requirements. As an alternative, this paper proposes a positioning framework based on Angle-of-Arrival (AoA) measurements. The framework incorporates a low-complexity AoA estimation algorithm derived from the analysis of the spatial covariance matrix. This procedure inherently generates a link quality metric which, alongside the AoA estimate, is utilized for final UE localization. The proposed localization algorithm belongs to the class of Weighted Least Squares (WLS) estimators and provides a unified approach to UE positioning in both 2D and 3D physical space. Simulation results demonstrate the effectiveness of the proposed framework under the challenging high-multipath conditions inherent to 5G indoor deployments.