Predictive Inter-Carrier Handoff for 5G Coverage Dead Zones
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Solution Overview
Problem
Existing 5G networks experience issues with dropped calls and poor network coverage, which current solutions fail to fully address, leading to suboptimal user experiences.
Innovation Solution
Implementing a system that uses machine learning to analyze user data from dropped calls and network problems, predicting future coverage issues, and initiating inter-carrier hand-offs to alternative carriers or roaming partners to prevent dropped calls using a distributed unit (DU) and central unit control plane (CU-CP) communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional 5G network coverage is used, then network infrastructure is simplified, but dropped calls and poor network coverage occur in problematic areas
Solution Approach 1:
The system performs preliminary analysis of historical call drop data and network performance metrics to identify problematic coverage areas before calls are dropped. Machine learning models predict future call drop locations, allowing the network to proactively initiate handoffs to alternative carriers or roaming partners before connectivity issues occur, thereby preventing dropped calls rather than reacting after they happen.
Solution Approach 2:
The patent introduces an intermediary predictive analytics platform that sits between the 5G network infrastructure and end-user devices. This intermediary system analyzes network data, predicts coverage problems, and orchestrates inter-carrier handoffs, acting as a mediator that improves reliability without requiring fundamental changes to the underlying 5G network infrastructure.
2Area of stationary object
If network coverage is expanded to eliminate dead zones, then service area is increased, but network cost and complexity increase
Solution Approach 1:
Instead of physically expanding network infrastructure into problematic areas, the system changes the parameter of carrier selection dynamically. By analyzing historical data and predicting coverage issues, the system switches the operational parameter from a single primary carrier to alternative carriers or roaming partners in predicted problematic zones, effectively expanding service coverage without physical infrastructure deployment.
Solution Approach 2:
The patent makes the network system multi-functional by enabling it to operate on multiple carriers and roaming partners. The predictive system allows a single 5G network infrastructure to universally serve users across different carrier networks depending on predicted coverage conditions, eliminating the need to build dedicated infrastructure in every geographic area while maintaining broad coverage.
3Reliability
If reactive call drop mitigation is used, then current problems are addressed, but user experience degradation has already occurred
Solution Approach 1:
The system performs preliminary analysis of historical call drop data and network performance metrics to identify problematic coverage areas before calls are dropped. Machine learning models predict future call drop locations, allowing the network to proactively initiate handoffs to alternative carriers or roaming partners before connectivity issues occur, thereby preventing dropped calls rather than reacting after they happen.
Solution Approach 2:
The patent implements a feedback loop where historical call drop data, network performance metrics, and handoff outcomes are continuously analyzed to improve prediction accuracy. This feedback mechanism allows the system to learn from past performance and progressively improve its ability to predict and prevent call drops, reducing user experience degradation over time as the system becomes more accurate.
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.