Wireless Network Access Node Selection With Shadow Sectors
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Solution Overview
Problem
Existing methods struggle to efficiently identify which wireless network access nodes a target communication device is likely to be served by, especially in urban environments with numerous nodes and limited data availability, often requiring compromising selection criteria and failing to account for blocked line of sight.
Innovation Solution
An iterative algorithm that selects a shortlist of candidate wireless network access nodes by identifying the closest node and excluding others in its shadow sector, using relative locations and angular sizes based on distance, to create a ranked list without requiring additional data on blocking features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all wireless network access nodes within range are included in the candidate list, then the completeness of candidate selection is improved, but the complexity of data processing and analysis increases significantly
Solution Approach 1:
The patent segments the set of all candidate access nodes into two distinct groups: a shortlist of high-probability candidates and a longlist of lower-probability candidates. This segmentation is achieved by iteratively selecting the closest node and excluding others in its shadow sector, thereby dividing the processing task into manageable portions and reducing overall complexity while maintaining reliability.
Solution Approach 2:
The patent extracts the most relevant candidate nodes from the complete set by applying selection criteria based on relative location and shadow sector analysis. This extraction process identifies and isolates the high-probability candidates that are most likely to serve the target device, removing unnecessary candidates from further consideration and reducing processing burden.
2Reliability
If selection criteria are relaxed to include more nodes, then the coverage of potential serving nodes is improved, but the accuracy of identifying the actual serving node decreases
Solution Approach 1:
The patent applies local quality by differentiating between high-probability and low-probability candidates through the shadow sector exclusion mechanism. Nodes in the shortlist are marked as high-probability candidates that warrant detailed analysis, while nodes in the longlist are marked as lower-probability candidates. This local differentiation maintains accuracy for the most likely candidates while preserving broader coverage through the longlist.
Solution Approach 2:
The patent performs partial action by focusing detailed analysis resources on the shortlist of high-probability candidates rather than attempting to analyze all candidates equally. This partial focus on the most relevant subset maintains high identification accuracy while the existence of the broader longlist ensures comprehensive coverage is not compromised.
3Measurement precision
If additional data on blocking features is collected to improve accuracy, then the precision of identifying the serving node is improved, but the data collection burden and time increase
Solution Approach 1:
The patent performs preliminary action by pre-computing shadow sectors and relative locations for all candidate nodes before the actual serving node identification process. This preliminary preparation creates a ready-to-use filtering framework that can quickly evaluate candidates without requiring real-time collection of blocking feature data, thereby maintaining precision while minimizing data collection time during actual operations.
Solution Approach 2:
The patent implements self-service by using the available relative location data to automatically generate shadow sector exclusions without requiring external data collection on blocking features. The system serves itself by deriving the necessary filtering information from basic geometric relationships between nodes and the target device, eliminating the need for additional time-consuming data collection efforts.
Data Source
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AI summary
A computer-implemented method comprising selecting a shortlist of candidate wireless network access nodes most likely to serve a target wireless communication device at a target location from a longlist of candidate wireless network access nodes, by iteratively: (i) moving a closest candidate wireless network access node, which is the candidate wireless network access node of the longlist closest to the target location, from the longlist to the shortlist; and (ii) deleting from the longlist any other candidate wireless network access nodes located within a shadow sector which encompasses that closest candidate wireless network access node, the shadow sector being a sector of a circle centred on the target location and encompassing all of the candidate wireless network access nodes on the longlist.