Robert bosch gmbh (20240163755). METHOD FOR PREDICTIVE CHANNEL SELECTION simplified abstract
Contents
- 1 METHOD FOR PREDICTIVE CHANNEL SELECTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR PREDICTIVE CHANNEL SELECTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD FOR PREDICTIVE CHANNEL SELECTION
Organization Name
Inventor(s)
Hugues Narcisse Tchouankem of Hemmingen (DE)
Marie-Theres Suer of Braunschweig (DE)
Maximilian Stark of Hamburg (DE)
METHOD FOR PREDICTIVE CHANNEL SELECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240163755 titled 'METHOD FOR PREDICTIVE CHANNEL SELECTION
Simplified Explanation
The abstract describes a method for training a reinforcement learning model to act as a channel switching agent in a wireless communication network. The method involves predicting the quality of service for different channels, using this prediction as input for the reinforcement learning model, switching channels if necessary based on the model's output, measuring the current quality of service, determining a reward for the model, and adapting the model based on the reward.
- Obtaining predicted quality of service indicators for channels in a wireless network
- Using these predictions as input for a reinforcement learning model for channel selection
- Initiating channel switching if the model suggests a different channel
- Measuring current quality of service for the communication link
- Determining a reward for the model based on the measurement results
- Adapting the reinforcement learning model based on the reward
Potential Applications
This technology could be applied in various wireless communication systems to optimize channel selection and improve overall network performance.
Problems Solved
This technology helps address issues related to channel selection in wireless communication networks, such as ensuring optimal quality of service and efficient use of available channels.
Benefits
The benefits of this technology include improved network performance, better quality of service for users, and automated optimization of channel selection.
Potential Commercial Applications
Potential commercial applications of this technology include telecommunications companies, IoT device manufacturers, and network infrastructure providers looking to enhance their wireless communication systems.
Possible Prior Art
One possible prior art in this field is the use of machine learning algorithms for channel selection in wireless networks. Researchers have explored various methods to optimize channel selection based on factors such as signal strength, interference, and network congestion.
Unanswered Questions
How does the method handle dynamic changes in network conditions?
The abstract does not specify how the method adapts to sudden changes in network conditions that may affect channel quality and selection.
What are the computational requirements for implementing this method in real-time systems?
The abstract does not provide information on the computational resources needed to deploy this method in real-time wireless communication networks.
Original Abstract Submitted
a method for training a reinforcement learning model forming a channel switching agent in a wireless communication network. the method includes: obtaining a predicted quality of service indicator for one or more channels of a wireless network for a future period of time; providing the predicted quality of service indicator as input to a reinforcement learning model which is configured to provide an output related to a channel selection for the first communication link; if the output indicates a selection of a new channel different from the currently active channel for the first communication link, initiating a channel switching procedure for the first communication link to a second channel; obtaining measurement results indicating a current quality of service for at least the first communication link; determining a reward for the reinforcement learning model based on the obtained measurement results; and adapting the reinforcement learning model based on the reward.
