US Patent Application 18017598. CONFIGURATION CONSIDERATIONS FOR CHANNEL STATE INFORMATION simplified abstract
Contents
CONFIGURATION CONSIDERATIONS FOR CHANNEL STATE INFORMATION
Organization Name
Inventor(s)
Pavan Kumar Vitthaladevuni of San Diego CA (US)
Alexandros Manolakos of Escondido CA (US)
Taesang Yoo of San Diego CA (US)
Naga Bhushan of San Diego CA (US)
June Namgoong of San Diego CA (US)
Jay Kumar Sundararajan of San Diego CA (US)
Krishna Kiran Mukkavilli of San Diego CA (US)
Tingfang Ji of San Diego CA (US)
CONFIGURATION CONSIDERATIONS FOR CHANNEL STATE INFORMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18017598 titled 'CONFIGURATION CONSIDERATIONS FOR CHANNEL STATE INFORMATION
Simplified Explanation
- This patent application describes a method for transmitting configuration parameters for training a neural network in wireless communication. - The network entity sends a training command to the UE (user equipment) in a wireless message. - The UE trains the neural network based on the received configuration from the network entity. - The configuration includes a period of time for training the neural network. - The period of time can indicate an action for the UE to perform when it expires. - The period of time can also indicate the periodicity of the neural network training.
Original Abstract Submitted
A network entity may transmit a configuration for neural network training parameters for wireless communication by the UE, and the UE may train the neural network at the UE based on the configuration received from the network entity. The network entity may transmit a training command in a wireless message to the UE, and the UE may train the neural network based on the received configuration in response to the received training command. The configuration may include a period of time associated with the training the neural network. The period of time may indicate an action for the UE to perform when the period of time expires, and/or indicate the periodicity of the neural network training.
