Cloud RAN Indoor Location via Image Data
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Solution Overview
Problem
Conventional indoor location determination methods in cloud radio access networks face challenges due to the complexity of modeling indoor radio propagation environments, which leads to inaccurate results and high costs associated with manual training processes.
Innovation Solution
A communication system that utilizes image data from cameras to determine a user's physical location within a site by associating signature vectors with image-based locations, allowing for automatic generation of mapping data without requiring explicit modeling of indoor environments or additional hardware on wireless devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional indoor location determination methods are used in cloud radio access networks, then location tracking capability is provided, but modeling complexity and cost increase due to the complexity of indoor radio propagation environments
Solution Approach 1:
The patent introduces image data from existing surveillance cameras as an intermediary to establish a mapping relationship between visual location and RF signature. This mediator avoids the need for complex radio propagation modeling by using visual information to train a machine learning model that correlates RF signatures with physical locations, thereby resolving the contradiction between location accuracy and modeling complexity
Solution Approach 2:
The patent creates a virtual mapping model that copies spatial relationships from the visual domain to the RF domain. By training a machine learning model with image-based location data and corresponding RF signature vectors, the system creates a simplified representation of the indoor environment that enables location determination without directly modeling complex radio propagation characteristics
2Measurement precision
If manual training processes are used to generate mapping data, then location determination accuracy is improved, but training time and costs increase
Solution Approach 1:
The system enables automatic generation of mapping data by leveraging existing surveillance camera infrastructure and automated image processing. The machine learning model is trained using image data that automatically captures user locations and corresponding RF signatures, eliminating the need for manual intervention in data collection and mapping generation, thereby reducing training time and costs while maintaining accuracy
Solution Approach 2:
The patent repurposes existing surveillance cameras for dual functionality: their original security monitoring role and the new role of providing location training data for the RF positioning system. This multi-functional use of existing infrastructure reduces the need for dedicated training equipment and manual processes, decreasing both time and cost投入 while achieving accurate location determination
3Reliability
If additional hardware or requirements are imposed on wireless devices, then location tracking capability is enhanced, but user burden and device complexity increase
Solution Approach 1:
The system uses existing surveillance cameras for dual purposes: their original security function and providing location training data. This eliminates the need for additional hardware on wireless devices while maintaining location tracking capability, as the mapping model is trained offline using visual data from the existing camera infrastructure
Solution Approach 2:
The patent creates a virtual mapping model that copies spatial relationships from the visual domain to the RF domain. This approach allows the system to determine physical locations using RF signatures without requiring any additional sensors, cameras, or hardware modifications on wireless devices, thereby maintaining ease of operation while enhancing location tracking capability
Data Source
AI summary
A communication system that provides wireless service to at least one wireless device is provided. The communication system includes a baseband controller communicatively coupled to a plurality of radio points and at least one image capture device at a site. The baseband controller is configured to determine a signature vector for a wireless device associated with a first user. The communication system also includes a machine learning computing system configured to determine an image-based location of the first user based on image data from the at least one image capture device. The communication system is also configured to determine mapping data that associates the signature vector with the image-based location of the first user.