Qualcomm incorporated (20240113795). WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS simplified abstract
Contents
- 1 WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS - 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
WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS
Organization Name
Inventor(s)
Tribhuvanesh Orekondy of Biel (CH)
Arash Behboodi of Amsterdam (NL)
Joseph Binamira Soriaga of San Diego CA (US)
WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240113795 titled 'WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS
Simplified Explanation
The patent application describes techniques for training and using machine learning models to estimate a representation of a channel between a transmitter and a receiver in a spatial environment.
- Machine learning model trained to estimate channel representation
- Based on transmitter and receiver locations in spatial environment
- Actions taken based on estimated channel representation
Potential Applications
This technology could be applied in:
- Wireless communication systems
- Internet of Things (IoT) devices
- Autonomous vehicles
Problems Solved
This technology helps to:
- Improve signal strength and quality in communication systems
- Optimize data transmission in complex spatial environments
Benefits
The benefits of this technology include:
- Enhanced communication reliability
- Increased data transfer efficiency
- Improved overall performance of wireless systems
Potential Commercial Applications
This technology could be used in:
- Telecommunications industry
- Smart home devices
- Automotive industry
Possible Prior Art
One possible prior art is the use of traditional channel estimation techniques in wireless communication systems.
What are the limitations of this technology in real-world applications?
The limitations of this technology in real-world applications may include:
- Accuracy of channel estimation in dynamic environments
- Computational complexity of machine learning models
How does this technology compare to traditional channel estimation methods?
This technology offers advantages such as:
- Adaptability to changing environments
- Potential for improved accuracy in channel estimation
Original Abstract Submitted
certain aspects of the present disclosure provide techniques and apparatuses for training and using machine learning models to estimate a representation of a channel between a transmitter and a receiver in a spatial environment. an example method generally includes estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on a location of a transmitter in a spatial environment, a location of a receiver in the spatial environment, and information about the spatial environment. one or more actions are taken based on the estimated representation of the channel.
