公开(公告)号：US20240114359A1

公开(公告)日：2024-04-04

申请号：US18467707

申请日：2023-09-14

Applicant: MEDIATEK INC.

Inventor： Ta-Yuan Liu ,  Hao Bi ,  CHIA-CHUN HSU

IPC: H04W24/02 ,  H04L41/16 ,  H04W36/08 ,  H04W76/10

CPC classification number: H04W24/02 ,  H04L41/16 ,  H04W36/08 ,  H04W76/10

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.

公开(公告)号：US20240107597A1

公开(公告)日：2024-03-28

申请号：US18469492

申请日：2023-09-18

Applicant: MEDIATEK INC.

Inventor： Abhishek Roy ,  Hao Bi ,  CHIA-CHUN HSU

IPC: H04W76/10 ,  H04W76/20

CPC classification number: H04W76/10 ,  H04W76/20

Abstract: This invention presents methods leveraging artificial intelligence and machine learning (AI/ML) models to enhance wireless communications efficiency in 5G/6G networks. The processes involve storing, configuring, and transferring AI/ML models within base stations and user equipment devices (UE), allowing for localized decision-making and improved network performance. Features include dynamic model activation/deactivation, model compression/decompression, and encoding/decoding method negotiation. Periodic or condition-driven model updates ensure responsiveness to network changes, while model replacements enable upgrades and iterations. The system facilitates seamless handovers between base stations, with information sharing about model capabilities and UE specifics. Model storage and configuration can also occur in the UE, empowering it for local decision-making in variable or challenging network conditions. The techniques contribute to significant performance, efficiency, and reliability improvements in 5G/6G wireless networks.