18072365. ARTIFICIAL INTELLIGENCE RADIO FUNCTION MODEL MANAGEMENT IN A COMMUNICATION NETWORK simplified abstract (Dell Products, L.P.)
Contents
- 1 ARTIFICIAL INTELLIGENCE RADIO FUNCTION MODEL MANAGEMENT IN A COMMUNICATION NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARTIFICIAL INTELLIGENCE RADIO FUNCTION MODEL MANAGEMENT IN A COMMUNICATION NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Commercial Applications
- 1.9 Prior Art
- 1.10 Frequently Updated Research
- 1.11 Questions about Machine Learning Model Optimization in Wireless Communication Systems
- 1.12 Original Abstract Submitted
ARTIFICIAL INTELLIGENCE RADIO FUNCTION MODEL MANAGEMENT IN A COMMUNICATION NETWORK
Organization Name
Inventor(s)
Gwenael Poitau of Montreal (CA)
Hamidreza Farmanbar of Ottawa (CA)
ARTIFICIAL INTELLIGENCE RADIO FUNCTION MODEL MANAGEMENT IN A COMMUNICATION NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18072365 titled 'ARTIFICIAL INTELLIGENCE RADIO FUNCTION MODEL MANAGEMENT IN A COMMUNICATION NETWORK
Simplified Explanation
A wireless user equipment sends learning model information to a network node to optimize radio functions. The node responds by sending a model management indication configuration back to the user equipment. The user equipment monitors learning model performance metrics and takes control actions based on the analysis of these metrics.
- User equipment transmits learning model information to a network node.
- Node sends model management indication configuration back to user equipment.
- User equipment monitors learning model performance metrics.
- Control actions are determined based on the analysis of metrics.
- Examples of control operations include deactivating or retraining learning models.
Potential Applications
This technology can be applied in various fields such as telecommunications, IoT devices, autonomous vehicles, and industrial automation where machine learning models are used to optimize performance.
Problems Solved
This technology addresses the need for real-time monitoring and control of learning model performance in wireless communication systems to ensure optimal functionality.
Benefits
- Improved efficiency and performance of wireless communication systems. - Real-time optimization of learning models. - Enhanced network reliability and stability.
Commercial Applications
Optimizing radio functions in telecommunications networks, improving performance in IoT devices, enhancing safety and efficiency in autonomous vehicles, and increasing productivity in industrial automation processes.
Prior Art
Researchers can explore prior art related to machine learning model optimization in wireless communication systems, network management, and control systems.
Frequently Updated Research
Stay updated on the latest advancements in machine learning model optimization for wireless communication systems to ensure optimal performance and reliability.
Questions about Machine Learning Model Optimization in Wireless Communication Systems
How does this technology improve the efficiency of wireless communication systems?
This technology improves efficiency by monitoring learning model performance metrics and taking control actions to optimize radio functions in real-time.
What are the potential commercial applications of this technology?
Potential commercial applications include telecommunications networks, IoT devices, autonomous vehicles, and industrial automation processes where learning models are used to enhance performance and reliability.
Original Abstract Submitted
A wireless user equipment transmits learning model information corresponding to learning models facilitating radio functions to a network node. In response, the node transmits to the user equipment a model management indication configuration corresponding to the learning model information. The user equipment monitors learning model parameter metrics, indicative of learning model performance. Monitored metrics may be used to determine a control action, or operation, to perform, based on analysis of the metrics with respect to model performance metric criterion. A model management indication may be transmitted to the user equipment indicating a determined control action. Examples of a control operation may comprise deactivating a currently operating learning model or retraining a learning model.
