18494940. METHOD FOR PREDICTIVE CHANNEL SELECTION simplified abstract (Robert Bosch GmbH)

METHOD FOR PREDICTIVE CHANNEL SELECTION

Organization Name

Robert Bosch GmbH

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 18494940 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 to select a channel, switching to a new channel if necessary, measuring the current quality of service, determining a reward based on the measurement results, and adapting the model accordingly.

• Obtaining predicted quality of service indicators for wireless network channels
• Using reinforcement learning model to select channels for communication links
• Initiating channel switching procedure based on model output
• Measuring current quality of service for communication links
• Determining reward for model based on measurement results
• Adapting reinforcement learning model based on reward

Potential Applications

The technology can be applied in various wireless communication systems to optimize channel selection and improve quality of service for users.

Problems Solved

This technology addresses the challenge of efficiently switching channels in a wireless network to maintain high quality of service for communication links.

Benefits

The benefits of this technology include enhanced network performance, improved user experience, and increased efficiency in channel selection processes.

Potential Commercial Applications

Potential commercial applications of this technology include telecommunications companies, network equipment manufacturers, and providers of wireless communication services.

Possible Prior Art

One possible prior art in this field could be related to existing methods for channel selection and optimization in wireless communication networks.

What are the potential impacts of this technology on network performance?

The technology can lead to improved network performance by optimizing channel selection based on predicted quality of service indicators.

How does this technology compare to traditional methods of channel switching in wireless networks?

This technology offers a more adaptive and intelligent approach to channel switching compared to traditional methods, as it utilizes reinforcement learning to make dynamic channel selection decisions based on predicted and measured quality of service indicators.

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.