UE Outage Tracking Across Base Stations for Real-Time Load Rebalancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for monitoring user equipment (UE) distribution in wireless networks fail to accurately reflect dynamic distributions, relying on historical analyses and manual searches when predictions fail, leading to inefficiencies in resource allocation.
Innovation Solution
A method and system that generates a data structure representing UE distribution by identifying UEs connected to out-of-service base stations, computing sets of seen and unseen UEs, and generating visualizations or updates to optimize network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current statistical analysis approaches are used to track UE distribution, then historical patterns can be identified, but real-time accuracy and reliability of UE distribution tracking deteriorate due to the highly dynamic nature of mobile devices
Solution Approach 1:
The system performs preliminary actions by proactively identifying UEs connected to base stations before those base stations go out of service. The mobility manager maintains pre-computed lists of UEs associated with each base station, enabling rapid response when outages occur without requiring real-time discovery during the actual outage event.
Solution Approach 2:
The patent introduces a mobility manager as an intermediary component that sits between base stations and the network core. This mobility manager collects, processes, and maintains UE connection information from multiple base stations, acting as a central coordinator that enables accurate tracking of UE distribution across the network without requiring direct communication between all network elements.
2Measurement precision
If manual searching methods are used to identify UE locations when predictions fail, then individual UE positions can be found, but network-wide distribution tracking efficiency deteriorates due to the time-consuming nature of manual searches
Solution Approach 1:
The system merges information from multiple base stations by collecting UE connection lists from several BSs and combining them into a comprehensive view. The mobility manager aggregates data from different geographic locations and base stations, creating a unified understanding of UE distribution across the entire network rather than treating each base station independently.
Solution Approach 2:
The patent creates copies of UE connection information by maintaining duplicate lists of UEs at the mobility manager that mirror the data stored at individual base stations. When a base station goes out of service, the mobility manager already has copied information about its connected UEs, eliminating the need for time-consuming manual searches to reconstruct this information.
3Measurement precision
If real-time tracking of highly dynamic UE distribution is implemented, then accurate network state information is obtained, but system complexity and computational requirements increase
Solution Approach 1:
The mobility manager performs preliminary actions by continuously maintaining updated lists of UEs connected to each base station before outages or changes occur. This pre-computation and pre-organization of data simplifies the response to dynamic changes, as the system already has structured information ready rather than needing to compute it from scratch during events.
Solution Approach 2:
The system implements self-service by having base stations automatically report their connected UE lists to the mobility manager without requiring external intervention. Each base station autonomously maintains and updates its own UE connection information, which is then automatically aggregated by the mobility manager, reducing the need for complex centralized monitoring and control mechanisms.
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: identifying a first base station (BS) that is out of service at a first time; identifying a first set of user equipment (UE) connected to the first BS in a pre-outage time window occurring before the first time; identifying a second set of UEs, the second set of UEs including UEs connected to other BSs in a wireless network; computing a set of seen UEs based on the second set of UEs and the first set of UEs; computing a set of unseen UEs based on the set of seen UEs and the first set of UEs; predicting a set of UEs that have left a service region that includes the first BS and the other BSs; computing a set of UEs without service based on the first set of UEs, the set of seen UEs, and the set of UEs that have left the service region; and generating a visualization of the set of UEs without service.