An Efficient Uplink 3D AoA Positioning Framework for 5G RedCap UEs in Indoor Factory Environments

Area of Science:

• Wireless communication
• Indoor positioning systems
• 5G network technology

Background:

• Reduced Capability (RedCap) User Equipment (UE) in 5G networks face positioning challenges.
• Limited bandwidth of RedCap devices hinders traditional time-domain ranging accuracy.
• Indoor positioning requires robust solutions for 5G applications.

Purpose of the Study:

• To develop an effective positioning framework for RedCap UE in indoor 5G environments.
• To overcome the accuracy limitations of existing methods for RedCap devices.
• To propose a hardware-efficient solution for indoor 5G positioning.

Main Methods:

• Utilizing uplink Angle-of-Arrival (AoA) measurements for positioning.
• Implementing a computationally efficient AoA estimation algorithm using spatial covariance matrix analysis.
• Employing a Weighted Least Squares (WLS) estimator for 2D and 3D localization.
• Generating a link quality metric alongside AoA for enhanced localization.

Main Results:

• The proposed framework achieves accurate positioning for RedCap UE using AoA.
• The method avoids complex beam scanning, simplifying hardware requirements.
• Numerical simulations validate the framework's efficacy in dense multipath 5G Indoor Factory (InF) environments.

Conclusions:

• The AoA-based framework offers a viable solution for RedCap UE positioning in indoor 5G networks.
• The approach enhances positioning accuracy and maintains hardware simplicity.
• This method addresses a critical need for precise indoor localization in 5G.