Predictive Inter-Carrier Handoff for Dropped Call Coverage Gaps
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Solution Overview
Problem
Existing 5G networks face challenges with dropped calls and poor network coverage, which current solutions have not fully addressed, leading to suboptimal user experiences.
Innovation Solution
Implementing a system that uses machine learning to analyze user data from dropped calls and network problems, predicts future coverage issues, and initiates inter-carrier hand-offs to prevent dropped calls by transferring devices to alternative carriers with superior service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network coverage methods are used, then network infrastructure is simplified, but dropped calls and poor coverage occur in problematic areas
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict future dropped call locations and times before they occur. The network proactively identifies problematic coverage areas and pre-configures handoff parameters, so when a device enters a predicted problematic zone, the handoff to alternative carriers has already been prepared and can execute immediately, preventing the dropped call before it happens.
Solution Approach 2:
The patent introduces an intermediary predictive analytics system that sits between the device and the network infrastructure. This intermediary layer analyzes historical call drop data, device locations, and network conditions to generate predictions about future coverage problems. It then mediates the handoff process by coordinating between the primary carrier network and alternative carriers, enabling seamless transitions without requiring complex changes to the core network infrastructure.
2Reliability
If machine learning prediction system is implemented, then dropped calls are prevented, but system complexity increases
Solution Approach 1:
The predictive system is segmented into distinct functional modules: data collection components that gather historical call drop information, machine learning model components that analyze patterns and generate predictions, and handoff execution components that implement carrier transitions. Each module operates independently with defined interfaces, allowing the system to be developed, deployed, and maintained in manageable parts rather than as a monolithic complex system.
Solution Approach 2:
The system implements feedback loops where actual handoff outcomes and call drop occurrences are continuously monitored and fed back into the machine learning models. This feedback mechanism allows the predictive algorithms to learn from real-world performance, continuously improving their accuracy over time without requiring manual reconfiguration or system redesign, thereby managing complexity through self-optimization.
3Measurement precision
If real-time data analysis is performed, then accurate predictions are achieved, but processing time and resources increase
Solution Approach 1:
The system performs preliminary data analysis by continuously training machine learning models on historical call drop data in the background, before real-time prediction is needed. Feature engineering and model updates are conducted in advance, so when real-time prediction is required, the system only needs to query pre-computed models with current device location and network conditions, dramatically reducing processing time while maintaining high prediction accuracy.
Solution Approach 2:
The predictive system dynamically adjusts its processing intensity based on network conditions and device states. During periods of low network stress, the system performs more comprehensive data analysis and model training to improve accuracy. When handoff execution time becomes critical, the system switches to lighter-weight prediction modes using pre-computed models, dynamically balancing accuracy requirements against time constraints without manual intervention.
Data Source
AI summary
Embodiments are directed towards systems and methods for a system for predictive inter-carrier hand-off to mitigate problematic coverage areas. One such method includes: training a machine learning model using the consolidating user data regarding dropped calls of the end user mobile devices and network problems from data logs as training data; analyzing the user data, using the machine learning model, to determine geographical areas in which repetitive dropped calls of the end user mobile devices or network problems have been identified; predicting, as an output from the machine learning model, future dropped calls of the end user mobile devices and network problems in identified geographical areas; analyzing alternative available carriers or roaming partners to determine whether they have superior service for end user mobile devices in the identified geographical areas; and initiating inter-carrier hand-off of an end user mobile device to another carrier or roaming partner.