Local ML Measurement Configuration for Handover Resource Management
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Solution Overview
Problem
Current wireless communication systems face challenges in optimizing handover decisions due to varying radio propagation characteristics in local areas, as existing measurement configurations fail to collect data from specific network areas necessary for machine learning-based radio resource management, leading to suboptimal handover performance.
Innovation Solution
A mechanism is introduced where a network node defines a validity area and measurement group for machine learning models, enabling UEs to perform targeted measurements within these areas, allowing for localized training and inference, thereby improving handover decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning based radio resource management is implemented, then network optimization and resource allocation are improved, but network complexity and computational requirements increase
Solution Approach 1:
The patent segments the radio resource management system into multiple distributed machine learning models deployed across different network entities (gNBs, edge servers, cloud platforms). Each model handles specific local decisions while coordinated through standardized measurement reporting, dividing the complex centralized problem into manageable distributed components that reduce overall system complexity.
Solution Approach 2:
The patent introduces standardized measurement reports as an intermediary mechanism between distributed machine learning models and network optimization. These reports aggregate local observations from multiple sources and transmit them to centralized or edge-based ML models, serving as a buffer that simplifies communication and coordination between distributed components.
2Measurement precision
If comprehensive measurement reporting is implemented for machine learning, then model accuracy and resource allocation quality are improved, but uplink signaling overhead and network traffic increase
Solution Approach 1:
The patent enables different gNBs and network entities to report measurements based on their local conditions and requirements. Each entity determines which measurements are most relevant to its local optimization needs, allowing tailored measurement reporting that maintains accuracy where needed while reducing redundant reporting elsewhere in the network.
Solution Approach 2:
The patent implements selective measurement reporting where only certain measurements or certain instances of measurements are reported based on predefined criteria such as threshold violations, changes in conditions, or specific event triggers. This partial action approach maintains measurement precision for critical parameters while significantly reducing overall signaling overhead by omitting redundant or less important measurements.
3Adaptability or versatility
If distributed machine learning models are deployed across multiple network entities, then system adaptability and local optimization are improved, but coordination complexity and synchronization requirements increase
Solution Approach 1:
The patent implements a universal standardized measurement report format that can be used across all distributed machine learning models and network entities. This single standardized interface serves multiple functions: it collects local measurements, transmits them to various ML models (centralized, edge, or local), and enables coordination between different distributed models, simplifying the complexity of inter-model communication while maintaining system adaptability.
4Speed
If real-time measurement reporting is implemented, then resource allocation responsiveness is improved, but processing load and energy consumption increase
Solution Approach 1:
The patent implements periodic measurement reporting combined with event-triggered reporting, where measurements are reported at regular intervals or when specific events occur (such as threshold violations or significant condition changes). This approach maintains real-time responsiveness for critical events while reducing continuous processing load and energy consumption during stable conditions, balancing responsiveness with energy efficiency.
Data Source
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AI summary
A method, apparatus, and a computer-readable storage medium are provided for a machine learning (ML) model based radio resource management. In one example embodiment, the method may include a network node defining a validity area and a measurement group for at least a ML model for one or more user equipments and transmitting the validity area and the measurement group of the at least one ML model to the user equipment.