Google llc (20240202528). WIRELESS SYSTEM EMPLOYING END-TO-END NEURAL NETWORK CONFIGURATION FOR DATA STREAMING simplified abstract
Contents
WIRELESS SYSTEM EMPLOYING END-TO-END NEURAL NETWORK CONFIGURATION FOR DATA STREAMING
Organization Name
Inventor(s)
Jibing Wang of San Jose CA (US)
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.