UE-Based Wireless Outage Detection for Near Real-Time 5G Coverage Gaps
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently detecting network outages in near real-time, particularly in high-density mobile broadband environments, which can impact user experience and network reliability.
Innovation Solution
Implementing apparatuses and methods for near real-time wireless network outage detection using 5G NR technology, which includes data collection and analysis at both UE and remote servers, enabling rapid identification and visualization of network outages through user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional wireless network monitoring methods are used, then system complexity is reduced, but detection speed and real-time responsiveness deteriorate
Solution Approach 1:
The patent segments the monitoring function by deploying distributed measurement entities at multiple network nodes (gNodeBs, eNodeBs, AMFs, SMFs) rather than relying on a single centralized system. Each node independently collects and reports metrics, enabling parallel processing and faster detection while distributing system complexity across multiple components.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and storing network metrics (signal strength, data throughput, connection status) in advance, rather than reacting to outages only when they occur. This pre-collection enables rapid detection and reduces the time needed to identify outage conditions when they arise.
2Measurement precision
If comprehensive network monitoring is implemented, then detection accuracy is improved, but data processing time and latency increase
Solution Approach 1:
The patent implements dynamic threshold adjustment where detection thresholds are not fixed but adapt based on current network conditions, time of day, and historical data patterns. This allows the system to maintain high detection accuracy across varying network states while reducing unnecessary processing delays by adjusting sensitivity levels dynamically.
Solution Approach 2:
The system employs feedback mechanisms where detected anomalies and outage conditions are fed back into the network management system to refine future detection algorithms. This continuous learning process improves accuracy over time while reducing processing time by eliminating false positives and optimizing detection criteria based on actual network behavior patterns.
3Reliability
If real-time data collection from multiple network nodes is performed, then outage detection responsiveness is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic data collection intervals rather than continuous monitoring at maximum capacity. Network nodes collect metrics at configured intervals and only increase sampling frequency when anomalies are detected, balancing the need for reliable outage detection with energy conservation during normal operating conditions.
Solution Approach 2:
The system dynamically changes monitoring parameters such as sampling frequency, data retention periods, and collection scope based on network conditions. During normal operation, parameters are set to minimize energy consumption, while automatically increasing monitoring intensity when outage indicators are detected, thus maintaining reliability without sustained high energy usage.
Data Source
AI summary
An apparatus of a UE is disclosed comprising one or more processors, coupled to a memory, configured to identify a location of the UE, determine a cellular coverage status of the UE as one of connected or limited coverage or no service, identify an MNC of the UE, and send, to a remote server, connectivity status information of the UE comprising the location, the cellular coverage status and the MNC of the UE. The remote server is configured to identify a network outage based on the connectivity status of the UE and a plurality of UEs located in one or more MNCs and send network outage information to the UE and the plurality of UEs associated with one of the MNCs identified with the network outage. The UE can decode the network outage information received from the remote server when the network outage is identified at the server.