18253427. TECHNIQUES FOR DETERMINING CHANNEL STATE INFORMATION USING A NEURAL NETWORK MODEL simplified abstract (QUALCOMM Incorporated)
TECHNIQUES FOR DETERMINING CHANNEL STATE INFORMATION USING A NEURAL NETWORK MODEL
Organization Name
Inventor(s)
Taesang Yoo of San Diego CA (US)
TECHNIQUES FOR DETERMINING CHANNEL STATE INFORMATION USING A NEURAL NETWORK MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18253427 titled 'TECHNIQUES FOR DETERMINING CHANNEL STATE INFORMATION USING A NEURAL NETWORK MODEL
Simplified Explanation
The patent application describes methods, systems, and devices for wireless communications. Here is a simplified explanation of the abstract:
- The user equipment (UE) receives indications about the number of antenna ports for reporting channel state information (CSI) and measuring CSI reference signals (CSI-RSs).
- The second number of antenna ports is smaller than the first number.
- The UE also receives indications about one or more neural networks to be used for determining the CSI associated with the first number.
- The UE determines the CSI using the neural networks and measurements made on the second number of antenna ports.
- The UE transmits a report including the CSI determined via the neural networks.
Potential Applications:
- Wireless communication systems
- Mobile devices
- Internet of Things (IoT) devices
Problems Solved:
- Efficient utilization of antenna ports for reporting CSI
- Accurate determination of CSI using neural networks
Benefits:
- Improved wireless communication performance
- Reduced overhead for reporting CSI
- Enhanced accuracy in determining CSI
Original Abstract Submitted
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a first indication of a first number of antenna ports for which the UE may report channel state information (CSI), and a second indication of a second number of antenna ports on which the UE may measure CSI reference signals (CSI-RSs). The second number may be less than the first number. The UE may receive a third indication of one or more neural networks to be used by the UE for determination of the CSI associated with the first number. The UE may determine the CSI using the one or more neural networks and using measurements made by the UE on the second number as inputs to the one or more neural networks. The UE may transmit a report including the CSI associated with the first number determined via the one or more neural networks.
