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 20240281348 titled 'MODEL MONITORING PROCEDURE FOR BEAM PREDICTION USE CASE

The abstract describes a patent application where a user equipment triggers model failure detection based on machine learning models or functionalities. The detection is initiated in a medium access control layer using an MFD window, with counts determined by model failure instances in the physical layer.

User equipment sends or receives information to trigger model failure detection.
Trigger is based on machine learning model or functionality.
MFD count is initiated in the medium access control layer.
MFD window is used in the medium access control layer.
MFD count is determined by model failure instances in the physical layer.
Model failure is determined when MFD count reaches a maximum prior to the end of the window.

Potential Applications: - Wireless communication systems - Network optimization - Fault detection in machine learning models

Problems Solved: - Efficient model failure detection - Improved network performance - Timely identification of model failures

Benefits: - Enhanced network reliability - Reduced downtime - Optimal utilization of machine learning models

Commercial Applications: Title: "Enhanced Network Performance through Model Failure Detection" This technology can be used in telecommunications, IoT devices, and industrial automation for improved system reliability and performance.

Questions about Model Failure Detection: 1. How does model failure detection impact network efficiency?

  - Model failure detection ensures timely identification of issues, leading to improved network performance and reliability.

2. What are the key benefits of using machine learning models for fault detection?

  - Machine learning models enable proactive identification of faults, enhancing system efficiency and reducing downtime.

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.