Proactive Wireless Handover via Predictive Mobility Analytics
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Solution Overview
Problem
Current handover techniques in wireless networks are reactive and inefficient, particularly in dense environments, leading to challenges in maintaining radio connectivity for a growing population of users and devices, with static mobility patterns and inadequate proactive and predictive methods.
Innovation Solution
A method for proactive and predictive handover in wireless networks that utilizes data analytics to predict user routes based on external network sources and user-specific behavior, calculating handover target probabilities and dynamically selecting optimal channels to ensure seamless service and maximize network throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive handover techniques are used, then the system is simple to implement, but handover success rate deteriorates in dense networks
Solution Approach 1:
The patent implements predictive handover by analyzing historical mobility patterns, navigation data, and public transportation schedules to pre-determine optimal handover targets before the UE actually needs to handover. This preliminary action allows the network to prepare handover resources in advance, significantly improving handover success rates in dense networks while managing complexity through automated algorithms.
Solution Approach 2:
The system dynamically adapts handover parameters based on real-time conditions by combining predictive analytics with live network measurements. The handover decision framework adjusts target cell selection, timing, and parameters based on predicted user trajectory, current signal conditions, and network load, optimizing handover performance for varying network densities and user scenarios.
2Adaptability or versatility
If static handover configuration is used, then the system is easy to manage, but it cannot adapt to varying user mobility patterns
Solution Approach 1:
The patent implements self-organizing network capabilities where the system automatically learns and adapts to user mobility patterns through continuous analysis of historical data, navigation information, and public transportation schedules. The handover configuration is dynamically optimized without manual intervention, allowing the system to adapt to varying mobility scenarios while reducing operational complexity through automated machine learning algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor handover performance and user mobility behavior, using this information to refine predictive models and adjust handover parameters. This closed-loop approach enables the system to adapt to changing mobility patterns over time while maintaining automated configuration management.
3Productivity
If traditional network planning is used, then the deployment process is straightforward, but it becomes cumbersome with millions of devices
Solution Approach 1:
The patent replaces traditional manual network planning and optimization processes with automated data analytics and machine learning systems. By substituting mechanical planning procedures with computational algorithms that analyze mobility patterns, navigation data, and network performance, the system efficiently manages deployment and optimization for millions of devices without proportionally increasing operational complexity.
4Reliability
If proactive predictive handover is implemented, then handover success rate improves, but network complexity increases
Solution Approach 1:
The system performs preliminary analysis of mobility patterns, navigation data, and public transportation schedules to predict future handover requirements before they occur. This advance preparation allows the network to pre-select target cells, allocate resources, and optimize handover parameters, improving success rates while managing complexity through automated predictive algorithms that process data efficiently.
Solution Approach 2:
The patent implements a universal handover framework that handles multiple scenarios (pedestrian, vehicular, public transportation) using a single predictive analytics system. The multi-functional approach consolidates complexity by providing a unified solution that adapts to different mobility patterns rather than requiring separate mechanisms for each scenario.
Data Source
AI summary
A system and method, for performing proactive handover in a wireless network. A connection may be established between at least one user equipment and the wireless network via a serving cell. Further, at least one of a public transportation data, an end-user population handover performance data, a navigation data, and an end user handover performance data is received and analyzed to predicting at least one wireless handover route. A handover target probability is calculated for the at least one predicted wireless handover route, and analyzed to determine a next serving cell for the user equipment. Upon detecting a condition for handover, the user equipment is proactively handed over from the serving cell to the next serving cell.