Cellular Edge Device Localization Without GPS Using Network ML
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Solution Overview
Problem
Current methods for determining the location of mobile edge devices are resource-intensive, power-consuming, and unreliable due to reliance on GPS, which can fail in adverse conditions or when inside structures, leading to increased costs and inefficiencies for telecommunications networks.
Innovation Solution
A device edge controller uses a machine learning model to combine edge parameters, geographic data, and real-time metadata to determine a mobile edge device's location, eliminating the need for GPS and reducing power consumption by leveraging cellular radio access and core networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used to determine device location, then location accuracy is improved, but power consumption increases and device complexity increases
Solution Approach 1:
The patent extracts the location determination function from the mobile edge device itself and relocates it to the network side (core network and RAN). By removing GPS hardware and continuous operation requirements from the device, the solution eliminates the power consumption burden while maintaining location accuracy through network-based tracking mechanisms.
Solution Approach 2:
The patent introduces network infrastructure (core network and RAN) as an intermediary to perform location determination. Instead of the device independently determining its location through GPS, the network acts as a mediator that tracks device position through cell tower triangulation and signaling data, thereby avoiding direct power consumption by the device's location system.
2Measurement precision
If GPS is used to determine device location, then location accuracy is improved, but device complexity increases
Solution Approach 1:
The patent removes GPS hardware and related location determination components from the mobile edge device, significantly reducing device complexity. The location functionality is extracted and implemented in the network infrastructure, allowing devices to be simpler and more cost-effective while still achieving accurate location tracking.
3Measurement precision
If GPS is used for location determination, then location accuracy is improved, but reliability decreases in adverse conditions
Solution Approach 1:
The patent uses the network infrastructure (core network and RAN) as an intermediary to provide location determination that is independent of GPS. This network-based approach uses cell tower triangulation and signaling data that remain reliable in adverse conditions such as urban canyons, indoor environments, or when GPS signals are blocked, thereby improving overall system reliability.
4Adaptability or versatility
If GPS component is added to mobile edge device, then location capability is improved, but computing resources and power consumption increase
Solution Approach 1:
The patent extracts the location capability from the mobile edge device and implements it in the network infrastructure. This allows devices to have minimal or no GPS hardware while still achieving accurate location tracking through network-based methods, significantly reducing power consumption and computational resource requirements at the device level.
Data Source
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AI summary
A device may receive edge parameters, geographic data, traffic data, and real-time metadata associated with an approximate location of a mobile edge device and a device edge. The device may receive a request for an actual location of the mobile edge device and the device edge, and may process the edge parameters, the geographic data, the traffic data, and the real-time metadata, with a machine learning model, to calculate multiple locations. The device may discard locations that fail to fit the edge parameters, the geographic data, the traffic data, and the real-time metadata, to generate a set of locations, and may select, from the set of locations, a location with a greatest location confidence determination as the actual location of the mobile edge device and the device edge.