Terrestrial Positioning for Unknown Cells via Network Entity
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G NR and other multi-access technologies, face challenges in positioning unknown cells that do not have sufficient observations for crowdsourced databases, leading to failures in terrestrial position techniques.
Innovation Solution
The system employs network entities to detect and identify cell locations based on cell IDs, estimate average locations of cells, and calculate the position of network nodes, enabling positioning for cells without prior database entries through on-the-fly crowdsourcing and terrestrial positioning services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If terrestrial positioning techniques are used for cells without sufficient observations in crowdsourced databases, then positioning coverage is improved, but positioning accuracy deteriorates due to lack of data
Solution Approach 1:
The system pre-calculates and stores expected cell location data in the crowdsourced database during network deployment and planning phases. This preliminary action ensures that when a UE encounters a new cell without observations, the network entity can retrieve pre-computed location information from the database, providing both coverage and accuracy without requiring real-time observations.
Solution Approach 2:
The network entity acts as an intermediary between the UE and the positioning system. When a UE reports an unknown cell, the network entity queries the crowdsourced database using cell identification information, retrieves pre-stored location data, and provides the positioning information to the UE. This intermediary approach enables accurate positioning for new cells by mediating between limited UE observations and comprehensive database information.
2Speed
If crowdsourced databases are used for positioning, then positioning speed is improved, but reliability deteriorates for new cells without database entries
Solution Approach 1:
Location information for cells is pre-calculated and stored in the crowdsourced database during network deployment before actual service operation. This preliminary action ensures that when UEs need positioning for new cells, the information is already available in the database, maintaining both fast positioning speed and high reliability without requiring real-time crowd observations.
Solution Approach 2:
The system prepares positioning data in advance for potential future cells during network planning and deployment. This beforehand cushioning ensures that when new cells are deployed or appear in the network, their positioning information is already buffered in the database, preventing reliability issues that would otherwise occur for cells without prior observations.
3Measurement precision
If network entities calculate locations based on average cell locations, then positioning accuracy for unknown cells is improved, but system complexity increases
Solution Approach 1:
The network entity automatically performs the calculation of average cell locations and updates the crowdsourced database without requiring manual intervention. The system self-services by continuously monitoring cell observations from UEs, computing average locations, and maintaining the database, thereby improving positioning accuracy for unknown cells while keeping operational complexity manageable through automation.
Solution Approach 2:
The system implements a feedback mechanism where UE positioning measurements and cell observations are continuously fed back to the network entity. This feedback enables the network entity to dynamically update average cell locations and refine positioning accuracy for unknown cells, with the complexity managed through automated feedback loops rather than manual processes.
Data Source
AI summary
Aspects presented herein relate to methods and devices for wireless communication including an apparatus, e.g., a device or a server. The apparatus may detect a set of cells associated with a network node, where each of the set of cells includes a cell ID, where the cell ID for each of the set of cells is associated with a node ID for the network node. The apparatus may also identify a location of each of the set of cells based on the cell ID for each of the set of cells. Additionally, the apparatus may estimate an average location of the set of cells based on the location of each of the set of cells. The apparatus may also calculate a location of the network node based on the average location of the set of cells.