Mobile Positioning Using Onboard Micro-BSA
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cellular positioning methods, such as UE-assisted (UE-A) and UE-based (UE-B) approaches, face challenges in network efficiency, positioning accuracy, battery life, and network congestion due to the need for frequent data downloads and uploads.
Innovation Solution
A mobile-based positioning system that utilizes a micro-BSA (base station almanac) stored onboard the user equipment (UE), allowing for the generation of improved assistance data and accurate position estimates without constant network interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UE-assisted positioning methods are used with frequent data downloads and uploads, then positioning accuracy can be maintained, but network congestion increases and battery consumption rises
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing base station almanac data locally in the UE, and pre-generating assistance data using machine learning models before positioning is needed. This eliminates the need for frequent real-time data downloads from the network, reducing network congestion while maintaining positioning accuracy.
Solution Approach 2:
A machine learning model acts as an intermediary between the base station almanac data and the positioning calculation. The model generates assistance data locally in the UE based on historical patterns, eliminating the need for direct real-time communication with the network for assistance data retrieval, thus reducing network traffic.
2Measurement precision
If UE performs frequent positioning calculations with network interaction, then positioning accuracy is maintained, but battery life decreases
Solution Approach 1:
The UE performs self-service by maintaining local base station almanac data and using embedded machine learning models to generate assistance data and perform positioning calculations autonomously without frequent network interactions. This significantly reduces battery consumption associated with network communication while maintaining positioning accuracy.
Solution Approach 2:
The system pre-loads base station almanac data and pre-trains machine learning models in the UE before positioning is needed. This preliminary preparation enables the UE to perform rapid positioning calculations locally without energy-intensive real-time network communications, reducing overall battery consumption.
3Ease of operation
If assistance data is downloaded from network server, then positioning can be performed, but network efficiency decreases
Solution Approach 1:
The system extracts and stores essential base station almanac data locally in the UE, separating this reference data from network dependencies. The UE can then generate assistance data locally using machine learning models, eliminating the need for frequent network downloads and improving network efficiency while maintaining positioning capability.
Solution Approach 2:
The base station almanac data is preliminarily downloaded and stored in the UE when network conditions are favorable, and machine learning models are pre-trained offline. This preliminary action enables the UE to operate autonomously during network congestion, improving overall network efficiency while maintaining positioning capability.
Data Source
AI summary
A method for estimating position of a mobile device which includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server. The OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA. A position estimate for the mobile device is determined based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells. The initial OTDOA assistance data may be generated by the micro-BSA based upon an initial seed estimate.