Distributed PRS Resource Selection for NR Positioning Accuracy
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Solution Overview
Problem
Legacy wireless communication networks face challenges in optimizing resource allocation for accurate user positioning in distributed communication environments, particularly in scenarios like Unmanned Aerial Systems (UAS), vehicular (V2X), and industrial Internet of Things (IIoT), where centralized approaches are inadequate and resource selection for positioning reference signals (PRS) is inefficient, leading to potential collisions and reduced accuracy.
Innovation Solution
The implementation of a mechanism that uses geographical location and mobility information to dynamically select resources for PRS transmission, including random, predefined, and geo-location-based resource selection, to improve the accuracy of user positioning and manage radio resources effectively in distributed systems, allowing for flexible and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized resource allocation is used for PRS transmission, then resource management is simplified, but positioning accuracy deteriorates in distributed communication environments
Solution Approach 1:
The patent divides the centralized resource allocation function into distributed segments where each transmitting device independently selects PRS resources based on its own geo-location and mobility information. This segmentation resolves the contradiction by enabling localized decision-making that improves positioning accuracy while maintaining manageable complexity through standardized procedures.
Solution Approach 2:
The patent introduces dynamic resource selection where transmitting devices adaptively choose PRS resources based on real-time geo-location and mobility conditions. This dynamic approach allows the system to respond to changing environmental factors, improving positioning accuracy without requiring complex centralized coordination.
2Ease of operation
If random resource selection is used for PRS transmission, then resource allocation is simple, but collision probability increases
Solution Approach 1:
The patent incorporates feedback mechanisms where transmitting devices use their geo-location and mobility information to inform resource selection decisions. This feedback loop reduces random collisions by aligning resource choices with actual transmission conditions while maintaining operational simplicity through automated decision rules.
Solution Approach 2:
The patent changes the selection parameters from purely random to geo-location and mobility-based criteria. This parameter transformation reduces collision probability by making resource selection dependent on spatial and temporal factors, while keeping the allocation process simple through standardized parameter mapping.
3Measurement precision
If frequent PRS transmissions are used to improve positioning accuracy, then positioning accuracy improves, but resource consumption increases
Solution Approach 1:
The patent implements dynamic PRS transmission scheduling where the transmission frequency is adapted based on mobility conditions. High-mobility devices transmit more frequently to maintain accurate positioning, while low-mobility devices transmit less frequently, optimizing resource consumption without sacrificing necessary positioning accuracy.
Solution Approach 2:
The patent changes transmission parameters based on geo-location and mobility state, adjusting PRS transmission frequency and resource allocation dynamically. This parameter adaptation ensures accurate positioning for moving devices while reducing resource consumption for stationary or slow-moving devices.
Data Source
AI summary
Embodiments disclosed herein are directed to new mechanisms of resource allocation for transmission of positioning or ranging (e.g., sounding) reference signals. The embodiments may provide flexible and/or efficient resource allocation, and may improve accuracy of user positioning. The techniques described herein may be applied for multiple use cases, including UAS, V2X, IIoT, etc.