UE Positioning via RF Transmit Characteristics
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Solution Overview
Problem
Current techniques for managing user equipment (UE) in private networks are inefficient due to their reliance on standard UE power classes, leading to poor network conditions caused by UE movement and obstructions, resulting in time-consuming and costly trial-and-error approaches to optimize UE positioning, which consumes computing and networking resources and causes interference.
Innovation Solution
A management system that allocates and positions UEs based on their radio frequency (RF) transmit characteristics, using machine learning models to determine whether to modify RF characteristics and reposition UEs to ensure optimal signal range and quality of service (QoS) in complex network setups, enabling the use of specialized RF characteristics and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard UE power classes are used for managing user equipment in private networks, then device compatibility and ease of operation are improved, but network performance and reliability deteriorate due to poor signal conditions caused by UE movement and obstructions
Solution Approach 1:
The system dynamically changes RF transmit parameters (power class, antenna pattern, beamforming settings) based on real-time network conditions, UE location, and signal quality measurements. This allows the UE to adapt its transmission characteristics to maintain reliable connectivity while moving through the private network environment, resolving the contradiction between ease of operation and network performance.
Solution Approach 2:
The patent implements dynamic UE allocation and positioning management where the system continuously monitors network conditions and automatically adjusts UE assignments and locations. This dynamic approach replaces static power class management with adaptive control that responds to changing environmental factors such as obstructions and signal interference, thereby maintaining both operational simplicity and network reliability.
2Reliability
If trial-and-error approaches are used to optimize UE positioning, then network performance may improve, but time consumption and resource usage increase significantly
Solution Approach 1:
The system performs preliminary RF conformance testing and characterization during UE manufacturing or onboarding to establish baseline performance data. This preliminary action eliminates the need for time-consuming trial-and-error positioning later, as the system already has pre-collected data about each UE's RF characteristics to make informed allocation and positioning decisions from the start.
Solution Approach 2:
The patent implements continuous feedback loops where the system monitors actual network performance metrics (signal quality, throughput, latency) and uses this feedback to automatically adjust UE positioning and RF parameter configurations. This closed-loop control replaces manual trial-and-error methods with automated adaptive optimization that achieves better performance faster by learning from real-time measurements.
3Reliability
If higher power RF transmit characteristics are used, then signal range and network coverage are improved, but compliance with health-related RF emission rules and energy efficiency deteriorate
Solution Approach 1:
The system applies directional antenna patterns and beamforming techniques to concentrate RF energy in specific directions toward target UEs or network infrastructure, rather than radiating uniformly in all directions. This local quality approach improves signal range in needed directions while reducing overall RF exposure and compliance issues in other areas, allowing higher effective power where needed without proportionally increasing harmful emissions everywhere.
Solution Approach 2:
The patent dynamically adjusts RF transmit parameters including power class, antenna pattern, and beamforming settings based on real-time requirements. When high signal range is needed, the system temporarily increases power or uses directional patterns, but automatically reduces these parameters when not needed, thereby achieving required coverage while minimizing overall RF emissions and maintaining health compliance throughout operation.
Data Source
AI summary
A device may receive user equipment (UE) data identifying a UE and a location of the UE, network data identifying base stations and locations of the base stations associated with the UE, and radio frequency transmit characteristics of the UE. The device may calculate a signal range of the UE based on the UE data, the network data, and the radio frequency transmit characteristics of the UE. The device may process the UE data, the network data, the signal range of the UE, and triggers, with a model, to determine that the radio frequency transmit characteristics of the UE should be modified. The device may cause the UE to modify the radio frequency transmit characteristics of the UE based on the model determining that the radio frequency transmit characteristics of the UE should be modified.