18496761. CROSS-NODE DEEP LEARNING METHODS OF SELECTING MACHINE LEARNING MODULES IN WIRELESS COMMUNICATION SYSTEMS simplified abstract (QUALCOMM Incorporated)
Contents
- 1 CROSS-NODE DEEP LEARNING METHODS OF SELECTING MACHINE LEARNING MODULES IN WIRELESS COMMUNICATION SYSTEMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CROSS-NODE DEEP LEARNING METHODS OF SELECTING MACHINE LEARNING MODULES IN WIRELESS COMMUNICATION SYSTEMS - 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 Original Abstract Submitted
CROSS-NODE DEEP LEARNING METHODS OF SELECTING MACHINE LEARNING MODULES IN WIRELESS COMMUNICATION SYSTEMS
Organization Name
Inventor(s)
Tianyang Bai of Mountain View CA (US)
Hua Wang of Basking Ridge NJ (US)
Junyi Li of Fairless Hills PA (US)
CROSS-NODE DEEP LEARNING METHODS OF SELECTING MACHINE LEARNING MODULES IN WIRELESS COMMUNICATION SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18496761 titled 'CROSS-NODE DEEP LEARNING METHODS OF SELECTING MACHINE LEARNING MODULES IN WIRELESS COMMUNICATION SYSTEMS
Simplified Explanation
The abstract of the patent application describes a method of wireless communication performed by a user equipment (UE) involving a decision-making module and machine learning modules.
- The method receives a configuration for the decision-making module from a network.
- The decision-making module is executed to determine a selection parameter for a configuration of at least one machine learning module.
- The configuration of the machine learning module is selected based on the selection parameter.
- The method reports a decision resulting from executing the decision-making module back to the network.
Potential Applications
This technology can be applied in various wireless communication systems to optimize machine learning module configurations based on network-provided parameters.
Problems Solved
This technology solves the problem of efficiently configuring machine learning modules in wireless communication systems to improve performance and decision-making processes.
Benefits
The benefits of this technology include enhanced decision-making capabilities, improved network efficiency, and optimized machine learning module configurations for better performance.
Potential Commercial Applications
One potential commercial application of this technology is in the telecommunications industry, where it can be used to enhance the performance of wireless communication systems through optimized machine learning module configurations.
Possible Prior Art
One possible prior art for this technology could be research or patents related to machine learning module optimization in wireless communication systems.
What are the specific machine learning modules used in this method?
The specific machine learning modules used in this method are not specified in the abstract. Further details on the types of machine learning modules and their functions would provide a clearer understanding of the technology.
How does the decision-making module determine the selection parameter for the machine learning module configuration?
The abstract does not provide specific details on how the decision-making module determines the selection parameter for the machine learning module configuration. Exploring the algorithm or methodology used in this process would shed light on the decision-making mechanism in this technology.
Original Abstract Submitted
A method of wireless communication is performed by a user equipment (UE). The method receives, from a network, a configuration for a decision making module. The method also executes the decision making module to determine a selection parameter for a configuration of at least one machine learning module. The method selects the configuration of the at least one machine learning module based on the selection parameter. Further, the method reports, to the network, a decision resulting from executing the decision making module.
