Invention Grant
- Patent Title: Machine learning for channel estimation
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Application No.: US16417492Application Date: 2019-05-20
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Publication No.: US10911266B2Publication Date: 2021-02-02
- Inventor: Yang Cao , Nihar Nanda
- Applicant: Parallel Wireless, Inc.
- Applicant Address: US NH Nashua
- Assignee: Parallel Wireless, Inc.
- Current Assignee: Parallel Wireless, Inc.
- Current Assignee Address: US NH Nashua
- Agent Michael Y. Saji; David W. Rouille
- Main IPC: H04L25/02
- IPC: H04L25/02 ; H04B7/0413

Abstract:
Systems and methods are disclosed for performing training using superimposed pilot subcarriers to determine training data. The training includes starting with a training duration (T) equal to a number of antennas (M) and running a Convolutional Neural Network (CNN) model using training samples to determine if a testing variance meets a predefined threshold. When the testing variance meets a predefined threshold, then reducing T by one half and repeating the running Convolutional Neural Network (CNN) model until the testing variance fails to meet the predefined threshold. When the testing variance fails to meet the predefined threshold, then multiplying T by two and using the new value of T as the new training duration to be used. Generating a run-time model based on the training data, updating the run-time model with new feedback data received from a User Equipment (UE), producing a DL channel estimation from the run-time model; and producing an optimal precoding matrix from the DL channel estimation.
Public/Granted literature
- US20190356516A1 Machine Learning for Channel Estimation Public/Granted day:2019-11-21
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