18551326. IMPROVING SIDELINK COMMUNICATION simplified abstract (Nokia Technologies Oy)
Contents
- 1 IMPROVING SIDELINK COMMUNICATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMPROVING SIDELINK COMMUNICATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Dynamic Bandwidth Allocation for Machine Learning Training
- 1.13 Original Abstract Submitted
IMPROVING SIDELINK COMMUNICATION
Organization Name
Inventor(s)
Luis Guilherme Uzeda Garcia of Massy (FR)
Alvaro Valcarce Rial of Massy (FR)
Guillaume Decarreau of Munich (DE)
IMPROVING SIDELINK COMMUNICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18551326 titled 'IMPROVING SIDELINK COMMUNICATION
Simplified Explanation
The method involves signaling an access node that a terminal device is available for machine learning training using data from sidelink communication. The access node provides a configuration for a machine learning bandwidth part, and negotiation with another terminal device is performed. Finally, machine learning training is executed using the data obtained from sidelink communication in the designated bandwidth part.
- Signaling an access node about terminal availability for machine learning training
- Receiving configuration for machine learning bandwidth part from the access node
- Negotiating with another terminal device for machine learning training
- Executing machine learning training using data from sidelink communication
Key Features and Innovation
- Utilization of sidelink communication data for machine learning training - Dynamic configuration of machine learning bandwidth part - Negotiation process with another terminal device for training - Efficient execution of machine learning training in the designated bandwidth part
Potential Applications
This technology can be applied in various fields such as: - Internet of Things (IoT) devices - Autonomous vehicles - Healthcare monitoring systems - Industrial automation processes
Problems Solved
- Efficient utilization of sidelink communication data for machine learning training - Dynamic allocation of bandwidth for machine learning tasks - Streamlining the negotiation process for collaborative training
Benefits
- Improved accuracy and efficiency in machine learning training - Enhanced utilization of available resources - Facilitation of collaborative training processes - Optimization of bandwidth allocation for specific tasks
Commercial Applications
Title: Dynamic Bandwidth Allocation for Machine Learning Training This technology can be utilized in industries such as: - Telecommunications for network optimization - Manufacturing for predictive maintenance - Healthcare for personalized treatment recommendations - Transportation for traffic management systems
Prior Art
Further research can be conducted in the field of machine learning training using sidelink communication data to explore existing technologies and innovations.
Frequently Updated Research
Stay updated on advancements in machine learning training techniques and bandwidth allocation strategies to enhance the efficiency and effectiveness of this technology.
Questions about Dynamic Bandwidth Allocation for Machine Learning Training
How does this technology improve the efficiency of machine learning training processes?
This technology optimizes bandwidth allocation and utilizes sidelink communication data, leading to enhanced training accuracy and resource utilization.
What are the potential applications of dynamic bandwidth allocation for machine learning training beyond the ones mentioned in the article?
This technology can also be applied in fields such as financial services for fraud detection, agriculture for crop monitoring, and energy management for predictive maintenance.
Original Abstract Submitted
A method comprising transmitting, to an access node, a first indication indicating that a first terminal device is available for machine learning training with data obtained from sidelink communication, receiving, from the access node, a second indication indicating a configuration for a machine learning bandwidth part, performing a machine learning training negotiation with a second terminal device in the machine learning bandwidth part, and executing the machine learning training with the data obtained from the sidelink communication in the machine learning bandwidth part.
