Multi-RAT Controller Handover Prediction Using Historical Data
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Solution Overview
Problem
Existing wireless devices face challenges in seamlessly transitioning between different radio access technologies (RATs) during handovers, particularly when moving between cellular and wireless data networks, leading to suboptimal performance and service disruptions.
Innovation Solution
Implementing a multi-RAT controller that utilizes historical data to optimize access and handover decisions by generating performance metrics and predicting future handover needs, allowing for proactive preparation and selection of the best network paths based on user device and network performance profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a device selects a network for communications based on current availability, then the device can maintain simple network selection logic, but the device cannot proactively optimize handover timing to improve communication quality
Solution Approach 1:
The system performs preliminary actions by measuring network conditions in advance and predicting future handover needs before actual handovers occur. The controller measures current network conditions, identifies potential handover candidates, and prepares handover decisions proactively based on predicted future states, thereby improving communication quality without reactive complexity
Solution Approach 2:
The system implements feedback mechanisms where the controller continuously monitors network conditions, compares them against thresholds and historical data, and adjusts handover decisions accordingly. This feedback loop enables the system to learn from past performance and optimize future handovers, improving reliability while managing complexity through data-driven decisions
2Reliability
If the device waits for network conditions to deteriorate before initiating handover, then the device can simplify handover triggering logic, but service disruptions occur during the transition
Solution Approach 1:
The system performs preliminary handover preparation by identifying suitable target networks and pre-establishing connection parameters before actual handover is needed. This advance preparation reduces the time required during actual transition and prevents service disruptions by ensuring the target network is ready to receive the connection immediately
Solution Approach 2:
The system provides beforehand cushioning by maintaining measurement and monitoring capabilities that are ready to trigger handovers at optimal moments. The controller continuously gathers network condition data and compares it against predetermined thresholds, creating a cushion of prepared information that enables smooth, disruption-free handovers when needed
3Productivity
If the device manually selects network paths, then the device has control over communication routing, but the device cannot leverage historical data to optimize future handovers
Solution Approach 1:
The system uses feedback from historical handover data and network performance metrics to automatically optimize future handovers. The controller analyzes past handover outcomes, network conditions, and user behavior patterns to learn optimal handover strategies, thereby improving productivity while managing complexity through automated data-driven decision-making
Solution Approach 2:
The system implements self-service by automatically performing handover optimization without requiring manual user intervention. The controller autonomously measures network conditions, predicts handover needs, selects optimal target networks, and executes handovers based on learned patterns from historical data, thereby improving efficiency while reducing the complexity burden on users
4Reliability
If the device uses current network conditions only for handover decisions, then the device can make simple real-time decisions, but the device cannot anticipate future handover needs
Solution Approach 1:
The system performs preliminary analysis of network conditions and historical data to predict future handover needs before they actually occur. By measuring current network conditions continuously and comparing them against historical patterns and thresholds, the controller can anticipate future handovers and prepare decisions in advance, improving prediction accuracy while reducing response time through proactive rather than reactive decision-making
Data Source
AI summary
Systems and methods for adaptive access and handover configuration based on historical data are provided. Access and handover decisions are optimized in a multiple radio access technology environment using historical data associated with network performance. Future needs for access and handovers are predicted using historical data associated with the user and historical data associated with network performance. Performance metrics are received periodically or continuously from nodes in one or more networks at a centralized controller. The centralized multi RAT controller correlates these performance metrics and determines predicted handovers for a user device. Preparations for the predicted handovers can then be made prior to the handover event.