PRS Request Configuration Using Channel Metrics for Faster Positioning
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Solution Overview
Problem
In radio communications networks, especially NR networks, User Equipment (UE) initiated positioning requests for Positioning Reference Signals (PRS) face uncertainties in resource allocation, leading to potential rejection and increased latency due to conflicting resource demands and high-priority services, which current methods fail to optimize effectively.
Innovation Solution
UEs employ a mapping model, such as a machine learning model, to determine optimal PRS configurations based on channel metrics and historical data, improving the probability of resource allocation acceptance by predicting network behavior and reducing latency through informed request generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UE initiates positioning requests with standard PRS configurations, then positioning functionality is provided, but the requests face rejection due to resource constraints
Solution Approach 1:
The patent applies parameter changes by modifying PRS configuration parameters (comb size, bandwidth size, repetition pattern, periodicity, duration, time and frequency offset) to create optimized positioning requests. The mapping model transforms channel metrics into adjusted configuration parameters that better match network resource availability, thereby increasing acceptance probability without excessive resource consumption.
Solution Approach 2:
The patent implements feedback mechanisms by using channel metrics (SNR, RSRP, path loss) and historical configuration data to inform the mapping model. This feedback loop allows the system to learn from past positioning requests and network responses, continuously optimizing configuration selections to improve acceptance rates while managing resource usage efficiently.
2Loss of time
If UE uses fixed PRS configurations for positioning requests, then implementation is simple, but latency issues occur due to request rejections and retries
Solution Approach 1:
The patent applies preliminary action by pre-calculating multiple candidate PRS configurations and their associated metrics before submitting positioning requests. The mapping model prepares optimized configuration options in advance based on current channel conditions and historical data, allowing the UE to submit well-prepared requests that are more likely to be accepted on the first attempt, thereby reducing latency without requiring complex real-time decision-making.
Solution Approach 2:
The mapping model serves as an intermediary between raw channel metrics and PRS configuration selections. It processes complex channel state information and historical data to generate optimized configuration recommendations, simplifying the UE's decision-making process while improving request acceptance rates and reducing latency through intelligent configuration selection.
3Measurement precision
If network accepts all UE positioning requests, then positioning accuracy is improved, but resource constraints are violated
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting PRS configuration parameters based on channel metrics and resource availability. The mapping model modifies comb size, bandwidth size, repetition patterns, and other parameters to create configurations that achieve acceptable positioning accuracy while consuming appropriate network resources, preventing both resource exhaustion and insufficient positioning precision.
Data Source
AI summary
Examples of the disclosure relate to an apparatus including: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform: receiving one or more reference signals; determining one or more metrics from the one or more received reference signals; generating an input for a mapping model based on the one or more metrics; and providing the input to the mapping model to enable the mapping model to create a list of a plurality of configurations for signals that enable positioning that could be undertaken by a network, based on the generated input, wherein the configurations include one or more parameters for signals that enable positioning.