Machine Learning for Channel Estimation

A non-transitory computer-readable medium containing instructions for providing machine learning for channel estimation which, when executed, cause a network system to perform steps comprising:

• performing training using superimposed pilot subcarriers to determine training data;
• the performing training comprising:
• starting with a training duration (T) equal to a number of antennas (M);
• 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 using training samples and reducing the T by one half until the testing variance fails to meet the predefined threshold;
• and
• 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.

A network system for providing machine learning for channel estimation, the system comprising:

• a base station;
• a HetNet Gateway (HNG) in wireless communication with the base station;
• a Self-Organizing Network (SON) in communication with the HNG;
• an external data source in communication with the SON;
• and
• a data lake in communication with the external data source and the HNG;
• wherein operational data is collected at the HNG and sent to the data lake;
• wherein models are developed using data lake data and external source data and deployed to the SON;
• wherein the SON includes internal models and external models which are used with the models developed using the data lake and external source data to provide input to the SON;
• the SON supplying the HNG with SON parameters;
• and
• wherein the models feed back data to the data lake.