Google llc (20240320481). COMMUNICATING A NEURAL NETWORK FORMATION CONFIGURATION simplified abstract
Contents
COMMUNICATING A NEURAL NETWORK FORMATION CONFIGURATION
Organization Name
Inventor(s)
Jibing Wang of San Jose CA (US)
Erik Richard Stauffer of Mountain View CA (US)
COMMUNICATING A NEURAL NETWORK FORMATION CONFIGURATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320481 titled 'COMMUNICATING A NEURAL NETWORK FORMATION CONFIGURATION
Simplified Explanation: The patent application describes techniques and apparatuses for enabling base station-user equipment messaging regarding deep neural networks in a wireless communication system. A network entity determines a neural network formation configuration for processing communications and communicates this configuration to user equipment, which configures a neural network based on the received information.
- Neural network formation configuration for deep neural networks in a wireless communication system
- Communication of configuration from network entity to user equipment
- Configuration of neural network by user equipment for processing communications
- Adaptation to changing operating conditions and improved information recovery
- Utilization of neural networks for information recovery in wireless communication systems
Potential Applications: - Wireless communication systems - Information recovery in changing operating conditions - Optimization of neural network configurations for processing communications
Problems Solved: - Adapting to changing operating conditions in wireless communication systems - Enhancing information recovery through neural network configurations
Benefits: - Improved adaptability to changing conditions - Enhanced information recovery capabilities - Efficient processing of communications in wireless networks
Commercial Applications: The technology can be applied in telecommunications, IoT devices, and other wireless communication systems to optimize information recovery and adapt to dynamic operating conditions, potentially leading to improved network performance and user experience.
Prior Art: Prior research in the field of neural networks and wireless communication systems may provide insights into similar approaches to optimizing information recovery and adapting to changing conditions.
Frequently Updated Research: Researchers are continually exploring new methods to enhance the performance of neural networks in wireless communication systems, focusing on improving information recovery and adaptability.
Questions about Deep Neural Networks: 1. How do deep neural networks differ from traditional neural networks in wireless communication systems? 2. What are the key challenges in implementing deep neural networks for information recovery in dynamic operating conditions?
Original Abstract Submitted
techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. a network entity (base station core network server ) determines a neural network formation configuration (architecture and/or parameter configurations ) for a deep neural network (deep neural network(s) ) for processing communications transmitted over the wireless communication system. the network entity (base station core network server ) communicates the neural network formation configuration to a user equipment (ue ). the user equipment (ue ) configures a first neural network (deep neural network(s) ) based on the neural network formation configuration. in implementations, the user equipment (ue ) recovers information communicated over the wireless network using the first neural network (deep neural network(s) ). this allows the wireless communication system to adapt to changing operating conditions and improve information recovery.
