18422254. MODEL MONITORING PROCEDURE FOR BEAM PREDICTION USE CASE simplified abstract (Nokia Technologies Oy)
MODEL MONITORING PROCEDURE FOR BEAM PREDICTION USE CASE
Organization Name
Inventor(s)
Keeth Saliya Jayasinghe Laddu of Espoo (FI)
MODEL MONITORING PROCEDURE FOR BEAM PREDICTION USE CASE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18422254 titled 'MODEL MONITORING PROCEDURE FOR BEAM PREDICTION USE CASE
Simplified Explanation: The patent application describes a method where user equipment triggers model failure detection based on machine learning models, with the goal of identifying model failures in a communication network.
Key Features and Innovation:
- User equipment sends or receives information to initiate model failure detection.
- Machine learning models are used to trigger model failure detection in the medium access control layer.
- Model failure instances in the physical layer determine the model failure count.
- Detection of model failure occurs when the model failure count reaches a maximum threshold within a specified window.
Potential Applications: This technology can be applied in various communication networks to improve the reliability and performance of machine learning models used for network optimization.
Problems Solved: This technology addresses the challenge of detecting model failures in real-time to prevent network disruptions and optimize network performance.
Benefits:
- Enhanced network reliability and performance.
- Real-time detection of model failures.
- Improved network optimization and efficiency.
Commercial Applications: The technology can be utilized in telecommunications, IoT networks, and other communication systems to ensure the smooth operation and performance optimization of machine learning models.
Prior Art: Researchers can explore prior art related to machine learning model failure detection in communication networks to understand the evolution of this technology.
Frequently Updated Research: Stay updated on the latest research developments in machine learning model failure detection in communication networks to leverage cutting-edge advancements in the field.
Questions about Model Failure Detection: 1. How does model failure detection impact the overall performance of communication networks? 2. What are the key challenges in implementing real-time model failure detection in machine learning models used in communication networks?
Original Abstract Submitted
In accordance with example embodiments of the invention, a user equipment receives or sends information to trigger model failure detection (MFD), wherein the trigger is based on a machine learning model or machine learning model functionality to initiate an MFD count in a medium access control layer, wherein MFD is using an MFD window in the medium access control layer and wherein one MFD count in the medium access control layer is determined by at least one model failure instances of the machine learning model or model functionality usage in a physical layer; and determining by the user equipment or a network the model failure for the machine learning model or machine learning model functionality when the MFD count in the medium access control layer is equal to or above a maximum model failure count prior to the end of the MFD window in the medium access control layer.
