17935006. WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS simplified abstract (QUALCOMM Incorporated)
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 17935006 titled 'WIRELESS CHANNEL RENDERING USING NEURAL NETWORKS
Simplified Explanation
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 involves estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on the locations of the transmitter and receiver in the spatial environment, as well as information about the spatial environment. Actions are then taken based on the estimated representation of the channel.
- Machine learning models used to estimate channel representation
- Training 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 in:
- Improving signal strength and reliability in wireless communication
- Enhancing the performance of IoT devices in various environments
- Optimizing communication between autonomous vehicles for safer operation
Benefits
The benefits of this technology include:
- Increased accuracy in estimating channel representations
- Enhanced communication efficiency
- Improved overall system performance
Potential Commercial Applications
This technology could be commercially applied in:
- Telecommunications industry
- Smart home devices
- Automotive industry
Possible Prior Art
One possible prior art for this technology could be:
- Research on machine learning models for wireless communication channel estimation
Unanswered Questions
How does this technology handle dynamic changes in the spatial environment?
This technology may need to adapt its channel estimation techniques to account for changes in the spatial environment, such as moving objects or varying signal interference.
What are the limitations of using machine learning models for channel estimation?
There may be challenges in training the models effectively with diverse spatial environments and ensuring real-time performance in estimating channel representations.
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.
