AI User Inference for Silent Cellular and IoT Outages
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Solution Overview
Problem
Existing systems struggle to identify mobile and IoT users impacted by cellular network outages, as most users remain silent and do not report service issues, making it difficult for network operators to proactively address and predict future outages.
Innovation Solution
A machine learning-based approach using key performance indicators (KPIs) to infer impacted users by analyzing patterns from a limited number of reported users, employing a lightweight model to detect silent users through critical KPIs like downlink packet loss rate and reference signal received power, and utilizing XGBoost for accurate user classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users report service issues manually, then network operators can identify impacted users, but most users remain silent and do not report, leading to incomplete identification
Solution Approach 1:
The patent introduces an inference model as an intermediary between network KPI data and user identification. This model mediates by analyzing network-level indicators (packet loss rate, reference signal received power, etc.) to infer which silent users are impacted by outages, bridging the gap between available network data and user-specific impact information without requiring direct user reports
Solution Approach 2:
The patent replaces the mechanical system of manual user reporting with an automated machine learning-based inference system. Instead of relying on users to physically or verbally report issues, the system automatically analyzes network KPIs and user profiles to identify impacted users, substituting human action with automated computational processes
2Measurement precision
If network operators monitor all users continuously, then they can identify impacted users accurately, but the complexity and cost of monitoring increases significantly
Solution Approach 1:
The patent applies partial action by monitoring only the critical KPIs necessary for outage detection (packet loss rate, reference signal received power, etc.) rather than all possible user parameters. The inference model processes only the subset of data needed to identify impacted users during outages, avoiding the complexity of continuous full-spectrum user monitoring while maintaining sufficient detection accuracy
Solution Approach 2:
The patent changes parameters by shifting from monitoring individual user-level details to monitoring network-level aggregate KPIs that indicate outages. By analyzing changes in network-wide parameters (packet loss rate, signal power) rather than individual user behaviors, the system achieves effective impacted user identification with reduced monitoring complexity
3Ease of manufacture
If traditional methods are used to identify outage users, then the system is simple to implement, but the ability to predict future issues and prevent outages is limited
Solution Approach 1:
The patent applies preliminary action by using the inference model to identify users at risk before complete outages occur. By analyzing trends in network KPIs and user profiles, the system can predict which users are likely to be impacted by upcoming outages, enabling proactive notifications and preventive measures before service degradation becomes severe
Solution Approach 2:
The patent implements feedback by using identified impacted users and outage patterns to continuously improve the inference model. The system learns from actual outage events and user responses, refining its predictions and detection accuracy over time, creating a self-improving system that becomes more reliable with deployment while maintaining relatively simple implementation
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving outage data about service issues including a service outage in a mobility network, the service outage affecting a plurality of affected users of the mobility network, the plurality of affected users including reported users, identifying, in the outage data, patterns about the reported users, and inferring impacted users of the mobility network based on the patterns about the reported users, the impacted users including users who experienced the service outage but did not report the service outage. Other embodiments are disclosed.