Terminal Resource Allocation via Computer Vision Tracking
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Solution Overview
Problem
Current UE positioning methods in 5G NR networks face challenges in accuracy and proactivity, especially during transitions between outdoor and indoor environments, and struggle with real-time adaptation to varying user mobility profiles, due to limitations in radio-only solutions and beam management.
Innovation Solution
Integration of computer vision systems with wireless networks to provide location and tracking information, enabling more accurate and proactive resource allocation by leveraging non-radio data for enhanced spatial awareness and mobility management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radio-only positioning methods are used in 5G NR networks, then the system maintains simplicity and relies solely on wireless communication infrastructure, but positioning accuracy deteriorates during transitions between outdoor and indoor environments
Solution Approach 1:
The patent combines radio-based positioning methods with computer vision-based positioning methods into a unified system. The network server receives positioning data from both radio measurements (signal strength, time of arrival) and computer vision measurements (image recognition, object detection), fuses these data sources, and generates composite positioning results. This merging enables accurate positioning across diverse environments including transitions between outdoor and indoor spaces, where each method compensates for the other's weaknesses.
2Productivity
If radio-only solutions are deployed, then the system architecture remains simple and radio-independent, but real-time adaptation to varying user mobility profiles becomes difficult
Solution Approach 1:
The positioning system is designed to handle multiple functions through a unified architecture: it performs both radio-based positioning and computer vision-based positioning, supports various mobility profiles (pedestrian, vehicular, stationary), and adapts to different environmental conditions (indoor, outdoor, transitional). The network server acts as a universal processing unit that can select and combine appropriate positioning methods based on the specific scenario, enabling real-time adaptation without requiring separate specialized systems for each function.
3Measurement precision
If computer vision systems are integrated with wireless networks, then positioning accuracy and tracking information improve significantly, but system complexity and infrastructure requirements increase
Solution Approach 1:
The network server functions as an intermediary that manages the integration between radio access networks and computer vision systems. It receives raw positioning data from both radio measurements and computer vision cameras, performs data fusion and processing, and delivers refined positioning results to the mobile device. This intermediary architecture allows the complex tasks of multi-source data integration and processing to be centralized, reducing the complexity burden on individual network elements and mobile devices while maintaining high tracking accuracy.
Data Source
AI summary
It is provided a method, including providing location information indicating a location of a terminal to a radio-independent localization and tracking system; evaluating at least one of environmental information and tracking information received from the radio-independent localization and tracking system with respect to the terminal in response to providing the location information; managing a resource for serving the terminal based on the at least one of the environmental information and the tracking information, wherein the environmental information includes information about an environment of the terminal, and the tracking information includes information about a track of the terminal.