UE Position Estimation Using Periodic Multi-Base-Station Reports
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Solution Overview
Problem
Existing position determination methods for user equipment (UE) in indoor or built-up environments face challenges in achieving high localization accuracy while minimizing signaling overhead, particularly in 5G networks where limited reporting of beam measurements from neighboring cells is optimized for signaling efficiency.
Innovation Solution
A method utilizing a machine-learning model, specifically a convolutional neural network and recurrent neural network, to estimate UE position based on reference signal received power (RSRP) measurements from serving and neighboring base stations, with enhanced measurement reports triggered at periodic intervals to enhance accuracy while reducing unnecessary reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enhanced measurement reports are triggered at periodic intervals to enhance localization accuracy, then localization accuracy is improved, but signaling overhead increases
Solution Approach 1:
The patent applies periodic action by triggering enhanced measurement reports at predetermined periodic intervals rather than continuously. The network node determines positioning needs and requests enhanced measurement reports at specific time intervals, achieving a balance between localization accuracy and signaling overhead reduction. This periodic triggering mechanism ensures that measurement data is collected sufficiently for accurate positioning while avoiding unnecessary continuous reporting that would increase signaling overhead.
2Measurement precision
If measurement data is collected from multiple base stations for accurate positioning, then localization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing measurement reporting into different types: normal measurement reports and enhanced measurement reports. Enhanced reports are requested only when needed for accurate positioning, while normal reports handle routine positioning. This segmentation allows the system to collect detailed multi-base station measurement data only when required, reducing overall device complexity and processing burden while maintaining the capability for high-accuracy positioning when needed.
3Reliability
If continuous position determination is performed to maintain location tracking, then positioning reliability is improved, but signaling overhead increases
Solution Approach 1:
The patent applies dynamics by making the measurement reporting mechanism adaptive rather than static. The network node dynamically determines when enhanced measurement reports are needed based on current positioning requirements, user location changes, and network conditions. This dynamic approach allows the system to maintain positioning reliability by requesting enhanced reports only when the user's position changes or accuracy is compromised, while avoiding unnecessary continuous reporting that would increase signaling overhead.
Data Source
AI summary
An apparatus, method and computer program are disclosed. The apparatus may include circuitry configured for receiving from a target device, at a first time instance, a set of first measurement data associated with each of a plurality of base stations and determining a first position of the target device based on the received first sets of measurement data. The circuitry may also receive from the target device, at each of one or more subsequent time instances, a second set of measurement data associated with one, or each of a smaller number, of the base stations and determining, at each of the one or more subsequent time instances, a respective position of the target device based on the position determined at a previous time instance and the second set of measurement data.