Inter-Device Positioning Coordination for Accurate NLOS Location
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Solution Overview
Problem
Existing positioning methods in telecommunication networks, particularly in non-line of sight conditions, suffer from inaccuracies due to the reliance on line of sight-based signaling, leading to inefficient and disruptive service provisioning.
Innovation Solution
A method utilizing a combination of Position Reference Signals (PRS) from network elements and Sidelink Reference Signals (SLRS) from known network devices, coupled with a machine-learned location inference model, to determine the position of user devices, enabling accurate positioning even in obstructed areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If line of sight based signaling methods are used for positioning, then positioning can be implemented in outdoor areas, but positioning accuracy deteriorates in indoor areas or outdoor areas with obstacles
Solution Approach 1:
The positioning method is segmented into multiple types (LOS-based positioning, NLOS-based positioning, and hybrid positioning) that can be selected based on the environment. The system divides the positioning approach according to line of sight conditions, allowing each segment to optimize for its specific condition rather than using a single method universally.
Solution Approach 2:
The system dynamically adapts the positioning method based on real-time conditions. The network element determines whether to use LOS-based or NLOS-based positioning by evaluating current signal conditions and environment characteristics, making the positioning approach flexible and responsive to changing conditions rather than static.
2Measurement precision
If machine learned location inference models are used, then positioning accuracy in non-line of sight conditions is improved, but device complexity increases
Solution Approach 1:
A machine learned location inference model acts as an intermediary between raw signal measurements and final position determination. The model processes complex NLOS signal characteristics and transforms them into accurate position estimates, handling the complexity internally while presenting a simplified interface to the rest of the system.
Solution Approach 2:
The system performs self-training of the machine learned model using historical positioning data and feedback. The network element continuously improves the model's accuracy by learning from actual positioning outcomes, reducing the need for external intervention and manual calibration while maintaining high positioning accuracy.
Data Source
AI summary
A method for adaptive positioning accuracy of devices in a telecommunication network may be provided. The method may be performed by one or more processors and may include transmitting, by a network element of the telecommunication network to a user device, a positioning trigger signal and a positioning reference signal; transmitting, by a user premise equipment to the user device, a sidelink reference signal to the user device; receiving, by the network element, a ranging result report, the ranging result report calculated by the user device including a distance and a timing information associated with signals received by the user device; and transmitting, by the network element to a core network element of the telecommunication network, a position of the user device, wherein the position of the user device is based on a machine learned location inference model and the ranging result report.