View source for Google llc (20240202528). WIRELESS SYSTEM EMPLOYING END-TO-END NEURAL NETWORK CONFIGURATION FOR DATA STREAMING simplified abstract

=WIRELESS SYSTEM EMPLOYING END-TO-END NEURAL NETWORK CONFIGURATION FOR DATA STREAMING=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:Jibing Wang of San Jose CA (US)|Jibing Wang of San Jose CA (US)]][[Category:Jibing Wang of San Jose CA (US)]]

[[:Category:Erik Richard Stauffer of Sunnyvale CA (US)|Erik Richard Stauffer of Sunnyvale CA (US)]][[Category:Erik Richard Stauffer of Sunnyvale CA (US)]]

==WIRELESS SYSTEM EMPLOYING END-TO-END NEURAL NETWORK CONFIGURATION FOR DATA STREAMING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240202528 titled 'WIRELESS SYSTEM EMPLOYING END-TO-END NEURAL NETWORK CONFIGURATION FOR DATA STREAMING

'''Simplified Explanation''': 

The patent application describes systems and techniques for training and implementing a chain of neural networks along a wireless transmission path to efficiently transmit data between devices without the need for individual coding and decoding processes.

* Neural networks are trained and implemented along a wireless transmission path.
* Source-side networks handle data encoding and channel encoding.
* Sink-side networks handle channel decoding and data decoding.
* End-to-end transmission is achieved without discrete coding and decoding stages.
* The process can adapt to various operational parameters.

'''Key Features and Innovation''': 

* Training and implementation of neural networks along a wireless transmission path.
* Integration of encoding and decoding processes within the neural network chain.
* Adaptability to different operational parameters for efficient data transmission.

'''Potential Applications''': 

* Wireless communication systems
* Data transmission in IoT devices
* Telecommunication networks

'''Problems Solved''': 

* Eliminates the need for separate coding and decoding processes.
* Enhances efficiency in data transmission.
* Facilitates adaptation to changing operational parameters.

'''Benefits''': 

* Streamlined data transmission process
* Improved efficiency in wireless communication
* Adaptability to different network conditions

'''Commercial Applications''': 

* Wireless communication technology for IoT devices
* Telecommunication networks optimization
* Data transmission systems for various industries

'''Prior Art''': 

Prior research in neural network-based data encoding and decoding processes in wireless communication systems.

'''Frequently Updated Research''': 

Ongoing research on neural network optimization for wireless data transmission.

'''Questions about Neural Network Chain for Wireless Data Transmission''': 

1. How does the integration of encoding and decoding processes within the neural network chain improve data transmission efficiency?
2. What are the potential challenges in adapting the end-to-end neural network chaining process to different operational parameters?


==Original Abstract Submitted==

systems and techniques provide for the joint training and implementation of an end-to-end chain of neural networks along the nodes of an at least partially wireless transmission path used to transmit a data stream between a data source device and at least one data sink device. the source-side neural networks of the chain can implement one or both of data encoding and channel encoding of outgoing data blocks, and the sink-side neural networks of the chain conversely can implement one or both of channel decoding and data decoding to provide efficient end-to-end transmission of the data stream without necessitating individual design, test, and implementation of discrete processes for each coding and decoding stage, while also facilitating the adaptation of the end-to-end neural network chaining process to various operational parameters.



[[Category:G06N3/082]]
[[Category:G06N3/04]]
[[Category:CPC_G06N3/082]]