Wireless Measurement Reporting for Range-Based UE Mobility Control

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Solution Overview

Problem

Existing wireless communication systems face challenges in efficiently managing user equipment (UE) mobility and optimizing resource allocation in radio access networks, particularly in heterogeneous environments with varying traffic loads and device capabilities.

Innovation Solution

Implementing artificial intelligence (AI) and machine learning (ML) techniques to predict UE trajectory and optimize resource allocation in radio access networks, utilizing AI/ML models to enhance mobility management and improve network performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional wireless communication systems manage UE mobility and resource allocation, then basic network operations are maintained, but network efficiency and performance optimization are insufficient in heterogeneous environments

Engineering Contradiction:

Improvenetwork efficiencyVSAvoidadaptability to heterogeneous environments

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent applies parameter changes by transitioning from traditional rule-based mobility management to AI/ML-based dynamic parameter optimization. The system continuously learns from network conditions and adjusts resource allocation parameters (such as bandwidth, power, and time resources) based on predicted UE trajectories and real-time channel conditions, enabling adaptive optimization in heterogeneous environments with varying traffic loads and device capabilities

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the mechanical/systematic approach of traditional resource allocation with AI/ML-based intelligent decision-making. Instead of relying on pre-configured rules and static algorithms, the system uses trained machine learning models to predict UE behavior patterns and optimize resource allocation dynamically, substituting deterministic mechanical systems with adaptive intelligent systems that can handle complexity in heterogeneous networks

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If AI/ML techniques are implemented to predict UE trajectory and optimize resource allocation, then network efficiency and data throughput are improved, but system complexity increases

Engineering Contradiction:

Improvedata throughputVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces AI/ML models as intermediary components between network measurements and resource allocation decisions. These models act as intelligent mediators that process raw measurement data (such as signal strength, channel quality, and UE location) and translate them into optimized resource allocation commands, simplifying the overall system architecture while enabling advanced optimization capabilities

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent applies preliminary action by using AI/ML models to predict future UE trajectories and channel conditions before actual resource allocation decisions are made. The system performs advance predictions of UE movement patterns and network conditions, allowing proactive resource allocation that optimizes performance before traffic demands arise, rather than reacting to conditions after they occur

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If measurement reporting is optimized based on predicted UE trajectory, then resource allocation accuracy is improved, but measurement and detection difficulty increases

Engineering Contradiction:

Improveresource allocation accuracyVSAvoidtrajectory prediction complexity

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The patent applies preliminary action by performing UE trajectory prediction using AI/ML models before resource allocation decisions are made. The system analyzes historical measurement data, current channel conditions, and UE behavior patterns to predict future UE locations and channel states, enabling proactive optimization of measurement reporting configurations and resource allocation based on anticipated conditions rather than reacting to past events

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where measurement results and resource allocation outcomes are continuously fed back to the AI/ML models for learning and refinement. The system uses closed-loop feedback to adjust trajectory predictions and measurement reporting strategies based on actual versus predicted performance, continuously improving measurement precision and resource allocation accuracy while adapting to changing network conditions and UE behaviors

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250374105A1Wireless Device Measurement Reporting in a Radio Access Network

Publication Date: 2025.12.04 OFINNO LLC

AI summary

A wireless device can receive, from a base station, one or more messages that include configuration parameters indicating a time interval and a range of values. The wireless device can send: an indication that a value is inside the range of values for an entire duration of the time interval; and/or an indication that the value is outside the range of values for the entire duration of the time interval. The wireless device can also or alternatively send one or more measurement reports, based on the value measured at the wireless device being inside or outside the range of values for the entire duration of the time interval.