Intel Corporation

Shoujiang Ma

Hujun Yin

RongZhen Yang

Xiaoran Fang

Matthew Hayes

Mehrzad Malmirchegini

John P Hogan

Aspects for machine learning-based link adaptation are described. For example, an apparatus can determine k-nearest neighbors (K-NNs) based on training data associated with the sub-band and on the signal to interference and noise ratio (SINR) of the sub-band. In aspects, the apparatus can identify a channel quality indicator (CQI) associated with the lowest error rate for the k-NNs and provide the identified CQI to a base station. In aspects, a neural network (NN) can provide labels for CQIs that indicate probability of choosing a CQI, and the CQI having highest probability will be provided to a base station. In aspects, a covariance matrix based on samples of a communication channel can be provided to a NN to determine a rank indicator (RI) corresponding to the channel, and channel state information associated with the (RI) can be sent to the base station. Other aspects are described.

Machine learning-based link adaptation

A method, system, medium, and article provide neural network loop filtering for video coding with multiple alternative neural networks.

Method and system of neural network loop filtering for video coding

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Shanghai, China

Shoujiang Ma