UE Positioning Signal Mapping from Reference Signals for PRS Acceptance
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Solution Overview
Problem
In radio communications networks, particularly 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, such as Ultra Reliability Low Latency (URLLC) services.
Innovation Solution
User Equipment (UE) employs a mapping model, potentially a machine learning model, to determine optimal PRS configurations based on channel metrics, historical data, and network feedback, to improve the probability of resource allocation acceptance by the network, thereby reducing latency and optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UE initiates positioning requests with PRS resource allocation, then positioning functionality is enabled, but resource allocation uncertainty increases leading to request rejection and increased latency
Solution Approach 1:
The UE performs preliminary channel measurements and evaluates multiple PRS configuration options before submitting the positioning request. The mapping model pre-processes channel metrics and historical data to predict which configurations are most likely to be accepted by the network, allowing the UE to submit optimized requests that reduce rejection rates and latency.
Solution Approach 2:
The system incorporates feedback loops where the UE receives network responses regarding PRS resource allocation decisions. This feedback, combined with historical data, is fed into the mapping model to continuously improve configuration predictions. The mapping model learns from past acceptance/rejection patterns to optimize future positioning requests.
2Adaptability or versatility
If multiple PRS configurations are supported, then positioning flexibility increases, but resource allocation complexity increases leading to conflicts with high-priority services
Solution Approach 1:
The mapping model evaluates multiple PRS configuration parameters (comb size, bandwidth size, repetition pattern, periodicity, duration, time and frequency offset) and selects optimal values based on channel conditions and historical acceptance data. By dynamically adjusting these parameters, the system achieves flexibility while managing complexity through intelligent parameter selection rather than exhaustive configuration exploration.
Solution Approach 2:
The UE autonomously performs channel measurements, generates metric inputs, and uses the mapping model to self-determine optimal PRS configurations without requiring extensive network coordination. This self-service approach reduces the burden on network resources and minimizes conflicts with high-priority services while maintaining configuration flexibility.
3Measurement precision
If mapping model uses historical data and channel metrics, then configuration accuracy improves, but processing requirements increase
Solution Approach 1:
The mapping model processes only the most relevant channel metrics and historical data features necessary for accurate configuration prediction, rather than analyzing all possible parameters. This selective processing approach maintains high accuracy in configuration selection while minimizing the computational energy required at the UE.
Data Source
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AI summary
Examples of the disclosure relate to an apparatus comprising: 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 comprise one or more parameters for signals that enable positioning.