18152082. REFERENCE SIGNALS SAMPLING AND IMPUTATION FOR ENABLING PARAMETER ESTIMATION VIA DEEP LEARNING simplified abstract (QUALCOMM Incorporated)
Contents
- 1 REFERENCE SIGNALS SAMPLING AND IMPUTATION FOR ENABLING PARAMETER ESTIMATION VIA DEEP LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 REFERENCE SIGNALS SAMPLING AND IMPUTATION FOR ENABLING PARAMETER ESTIMATION VIA DEEP LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Wireless Communication Technology
- 1.11 Original Abstract Submitted
REFERENCE SIGNALS SAMPLING AND IMPUTATION FOR ENABLING PARAMETER ESTIMATION VIA DEEP LEARNING
Organization Name
Inventor(s)
Mohamed Fouad Ahmed Marzban of San Diego CA (US)
Wooseok Nam of San Diego CA (US)
Taesang Yoo of San Diego CA (US)
REFERENCE SIGNALS SAMPLING AND IMPUTATION FOR ENABLING PARAMETER ESTIMATION VIA DEEP LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18152082 titled 'REFERENCE SIGNALS SAMPLING AND IMPUTATION FOR ENABLING PARAMETER ESTIMATION VIA DEEP LEARNING
Simplified Explanation
The patent application describes a method for wireless communication using machine learning models to estimate parameters.
- Receiving a reference signal from a network node.
- Determining an input signal based on the reference signal.
- Training a machine learning model with signals of the second type.
- Applying the input signal to the machine learning model to estimate parameters used in wireless communications by a user equipment (UE).
Key Features and Innovation
- Utilizes machine learning models to estimate parameters in wireless communications.
- Enhances the accuracy and efficiency of parameter estimation.
- Improves the performance of user equipment in wireless networks.
Potential Applications
- Wireless communication systems.
- Mobile networks.
- Internet of Things (IoT) devices.
- 5G and beyond networks.
Problems Solved
- Inaccurate parameter estimation in wireless communications.
- Limited efficiency in estimating parameters using traditional methods.
- Challenges in optimizing performance of user equipment in wireless networks.
Benefits
- Enhanced accuracy in parameter estimation.
- Improved efficiency in wireless communications.
- Optimized performance of user equipment.
- Potential for faster data transmission in wireless networks.
Commercial Applications
Wireless communication equipment manufacturers can integrate this technology to improve the performance of their devices, leading to better user experience and increased market competitiveness.
Questions about Wireless Communication Technology
How does machine learning improve parameter estimation in wireless communications?
Machine learning models can analyze complex patterns in data to make accurate predictions, allowing for more precise estimation of parameters in wireless communications.
What are the potential challenges in implementing machine learning models for wireless communication?
Some challenges may include the need for large amounts of training data, computational resources, and ensuring the models are robust and reliable in real-world scenarios.
Original Abstract Submitted
Disclosed is a method for wireless communication. The method comprises receiving, from a network node, a reference signal (RS) of a first type. An input signal of a second type is determined based on the reference signal. Signals of the second type are associated with training a first machine learning (ML) model applied by a UE to estimate a parameter used in wireless communications by the UE. The input signal of the second type is applied to the first ML model to estimate the parameter used in wireless communications by the UE.
