Signal Power Pattern Location Detection for Small Cell Placement
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Solution Overview
Problem
Current methods for determining the location of user equipment (UE) in a cellular network are inaccurate due to their reliance on static calibration points and ignore the complete set of visible base stations, leading to errors in small cell placement and capacity augmentation, which can result in suboptimal placement and increased RF interference.
Innovation Solution
The implementation uses signature areas and machine learning-based clustering to accurately determine UE locations by analyzing the set of visible base stations and their signal strengths, providing a dynamic and environment-agnostic solution that recommends optimal small cell placement without exposing Customer Proprietary Network Information (CPNI).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static calibration points are used for location determination, then the system complexity is reduced, but location accuracy deteriorates
Solution Approach 1:
The patent transitions from static calibration points to dynamic signature areas that are continuously updated based on real-time signal strength measurements from multiple base stations. The signature areas are dynamically adjusted as UEs move and network conditions change, improving location accuracy without requiring complex manual calibration procedures
Solution Approach 2:
The patent creates a virtual representation (signature area) that copies the essential characteristics of the physical radio environment. Instead of using actual physical calibration points, the system creates mathematical models of signal propagation patterns that can be efficiently stored and queried, reducing system complexity while maintaining accuracy
2Measurement precision
If the complete set of visible base stations is considered, then location accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the service area into distinct signature areas, each associated with a unique combination of visible base stations. This segmentation allows the system to pre-process and store location-signal strength relationships in an organized manner, reducing the computational complexity of real-time location determination while utilizing information from all visible base stations
Solution Approach 2:
The patent performs preliminary clustering and signature area definition during offline preparation or low-traffic periods. By pre-processing the data and creating the signature area database in advance, the system reduces the computational burden during real-time operation, allowing accurate location determination using complete base station sets without excessive processing complexity
3Productivity
If small cells are placed without accurate location data, then deployment speed is increased, but RF interference increases due to suboptimal placement
Solution Approach 1:
The system enables automatic small cell placement recommendations by using the signature area database to self-determine optimal locations without requiring manual site surveys or trial-and-error deployment. The network automatically identifies suitable locations based on signal strength patterns and coverage requirements, improving deployment speed while ensuring optimal placement to minimize RF interference
Solution Approach 2:
The patent implements a feedback mechanism where location measurements from UEs continuously refine the signature area database, which in turn improves future small cell placement recommendations. This closed-loop system ensures that placement decisions are based on the most current network conditions and actual signal propagation characteristics, reducing RF interference while maintaining rapid deployment capability
Data Source
AI summary
Systems and methods determine the location of user equipment (UE) in a radio access network (RAN). A network device collects UE location indications and corresponding received signal strength measurements for wireless access stations concurrently visible to cooperating UE devices at the indicated locations. Each UE location indication and corresponding received signal strength measurements provide a data point. The network device assigns signature areas having the same combination of the concurrently visible wireless access stations; identifies multiple clusters of data points in the signature areas; assigns a location value, which represents a geographic area, to each cluster of the multiple clusters; and determines an estimated location of another UE device, wherein the estimated location includes the location value of one of the multiple clusters based on concurrently visible wireless access stations and corresponding received signal strength measurements reported by the other UE device.