Context-Aware Handover Prediction for 5G Latency Reduction

Solution Overview

Problem

Current handover mechanisms in wireless communication systems, particularly between different network types, introduce significant latency and inefficiency, leading to perceptible delays and potential loss of connectivity, especially in dynamic environments like those enabled by 5G networks.

Innovation Solution

A contextual awareness-based handover prediction scheme using machine learning and AI models to proactively select the most optimal network, radio access technology, and network access node based on predicted target locations, implementing a 'make before break' strategy to minimize latency and ensure seamless connectivity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional handover mechanisms are used to transfer communication sessions between network access nodes, then connectivity is maintained during movement, but significant latency and delay are introduced (ranging from milliseconds to hundreds of milliseconds)

Engineering Contradiction:

Improveconnectivity maintenanceVSAvoidhandover latency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting the future location of the mobile device using machine learning models and proactively establishing connections with target network access nodes before the device actually moves into their coverage areas. This predictive approach allows the handover to be prepared in advance, significantly reducing the actual handover latency while maintaining connectivity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical handover mechanisms (which rely on real-time signal strength monitoring and reactive switching) with an AI-based predictive system. Machine learning models analyze historical movement patterns, current location data, and network conditions to predict future positions and optimize handover timing, substituting reactive mechanical processes with intelligent predictive control.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If vertical handover between different radio access technologies is implemented to provide network flexibility, then adaptability to different network conditions is improved, but even more delay and latency are introduced compared to horizontal handover

Engineering Contradiction:

Improvenetwork flexibilityVSAvoidhandover delay

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting the future location of the mobile device using machine learning models and proactively establishing connections with target network access nodes before the device actually moves into their coverage areas. This predictive approach allows the handover to be prepared in advance, significantly reducing the actual handover latency while maintaining connectivity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent dynamically changes multiple parameters including prediction time horizons, model weights, and handover thresholds based on device movement patterns, network conditions, and service requirements. This allows the system to optimize the balance between adaptability and latency by adjusting parameters such as how far in advance to initiate handover preparation based on detected movement speed and direction.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If reactive handover based on current signal conditions is used, then simple decision-making is maintained, but handover decisions are made after delay when the device is already moving towards poor connectivity

Engineering Contradiction:

Improvedecision-making simplicityVSAvoidreactive delay

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The patent replaces traditional mechanical handover mechanisms (which rely on real-time signal strength monitoring and reactive switching) with an AI-based predictive system. Machine learning models analyze historical movement patterns, current location data, and network conditions to predict future positions and optimize handover timing, substituting reactive mechanical processes with intelligent predictive control.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables the mobile device to serve itself by implementing autonomous predictive handover at the device level. The machine learning models run locally or with minimal network support, allowing the device to independently predict its own movement trajectory and initiate handover preparations without complex network-side coordination, thereby reducing overall system complexity while improving timing.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11963051B2Context aware handovers

Publication Date: 2024.04.16 INTEL CORP

AI summary

Disclosed embodiments provide a handover prediction scheme that is based on contextual awareness of a compute node, such as a mobile device. The contextual information is used to predict network availability in a predicted target location, which is an area that a compute node is likely to travel. The use of sensors embedded in or accessible by the compute node may be used to carry out aspects of the embodiments. A reinforcement learning recommendation model is used to determine an optimal network, radio access technology, and/or network access node to connect with ahead of arriving at the predicted target location at a predicted arrival time. Other embodiments are described and/or claimed.