Abstract: Apparatus and methods are provided for AI-ML model storage and transfer in the wireless network. In one novel aspect, the AI-ML model is stored at the AI server and transferred through the user plane (UP). In one embodiment, UE downloads the AI-ML model from the AI server through the UP connection. In one embodiment, the AI-ML model is updated at the RAN node, and the UE downloads the AI-ML model through the AI server. In another embodiment, the AI-ML model is updated at the UE, and the UE uploads the AI-ML model to the AI server through the UP connection. In another embodiment, the UE uploads the AI-ML model to the RAN through the AI server. In one embodiment, the UE mobility triggers the AI-ML model transfer. In one novel aspect, the AI dataset is shared and transferred among different entities through the UP connection or a new AI plane.

Abstract: The present disclosure provides a vector quantization based secret key generation device and method. The vector quantization based secret key generation device includes: a channel estimator for estimating a channel based on received signals to generate an estimated channel vector; a channel decorrelator for decorrelating entries of the estimated channel vector to generate a decorrelated estimated channel vector; a plurality of clustered vector quantizers (CVQs) each for quantizing the decorrelated estimated channel vector into a secret key or a secret key index; and a selector for selecting an optimal quantizer output from the plurality of CVQs and determining whether to discard the decorrelated estimated channel vector to reduce key disagreement probability (KDP). Therefore, the present disclosure provides a secret key generation technique that is capable of increasing key entropy and reducing KDP.