US Patent Application 18158885. MACHINE LEARNING FOR WIRELESS CHANNEL ESTIMATION simplified abstract
Contents
MACHINE LEARNING FOR WIRELESS CHANNEL ESTIMATION
Organization Name
Inventor(s)
Fabio Valerio Massoli of Amsterdam (NL)
Arash Behboodi of Amsterdam (NL)
Hamed Pezeshki of San Diego CA (US)
Joseph Binamira Soriaga of San Diego CA (US)
Taesang Yoo of San Diego CA (US)
MACHINE LEARNING FOR WIRELESS CHANNEL ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18158885 titled 'MACHINE LEARNING FOR WIRELESS CHANNEL ESTIMATION
Simplified Explanation
The patent application describes a method and system for wireless channel estimation using machine learning.
- The technique involves processing a sensing matrix using a machine learning model with multiple layers.
- The machine learning model uses a learned sparsifying dictionary to generate sparse vector representations.
- The final layer of the machine learning model outputs a channel estimation based on the processed sensing matrix.
- This approach improves the accuracy of wireless channel estimation in wireless communication systems.
- The use of machine learning allows for more efficient and effective estimation of wireless channels.
- The technique can be applied to various wireless communication technologies, such as Wi-Fi, cellular networks, and IoT devices.
Original Abstract Submitted
Certain aspects of the present disclosure provide techniques and apparatus for wireless channel estimation using machine learning. A sensing matrix is processed using a set of one or more layers of a machine learning model, based on a learned sparsifying dictionary, to generate a set of associated sparse vector representations. A channel estimation is determined based on output of a final layer of the set of one or more layers of the machine learning model.
