18365874. CODEBOOK FOR AI-ASSISTED CHANNEL ESTIMATION simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
CODEBOOK FOR AI-ASSISTED CHANNEL ESTIMATION
Organization Name
Inventor(s)
Xiaowen Tian of Raleigh NC (US)
Jianzhong Zhang of Plano TX (US)
CODEBOOK FOR AI-ASSISTED CHANNEL ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18365874 titled 'CODEBOOK FOR AI-ASSISTED CHANNEL ESTIMATION
Simplified Explanation
The method described in the patent application involves identifying ACF (Auto-Correlation Function) information by:
- Obtaining channel information with multiple channels of expected operation scenarios.
- Determining MMSE (Minimum Mean Square Error) channel estimation weights in the form of ACFs, SNR, and covariance matrices based on the channel information for each channel.
- Clustering the MMSE CE weights into K clusters, where the center ACF weight of each cluster represents a codeword.
- Determining a distance metric based on cluster distances after re-clustering.
- Iteratively re-clustering the ACF information if cluster distances before and after clustering differ significantly, updating center ACF weights and cluster distances.
- Generating a codebook with an index k for each of the K clusters and the center ACF weight of each cluster.
Potential applications of this technology:
- Wireless communication systems
- Signal processing algorithms
- Antenna array systems
Problems solved by this technology:
- Efficient channel estimation in wireless communication systems
- Improved signal processing accuracy
- Enhanced performance of antenna array systems
Benefits of this technology:
- Increased reliability of communication systems
- Higher data transmission rates
- Better utilization of available spectrum resources
Original Abstract Submitted
A method includes identifying ACF information by: obtaining channel information including multiple channels of expected operation scenarios; and based on the channel information for each of the channels, determining MMSE channel estimation (CE) weights expressed in a form of ACFs and an SNR, and covariance matrices. The method includes clustering the MMSE CE weights into K clusters. A center ACF weight of each of the K clusters represents a codeword. The method includes determining a distance metric based on a cluster distance after a re-clustering. The method includes, in response to a determination that cluster distances before and after the clustering differ from each other by a non-negligibly, iteratively re-clustering the ACF information thereby updating the center ACF weights and cluster distances. The method includes generating the codebook to include an index k of each of the K clusters and the center ACF weight of each of the K clusters.
