ENHANCING WIRELESS COMMUNICATIONS EFFICIENCY IN 5G/6G NETWORKS THROUGH AI/ML MODEL MANAGEMENT AND DEPLOYMENT

    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.

    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.

    AI-ML MODEL STORAGE IN OTT SERVER AND TRANSFER THROUGH UP TRAFFIC

    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.

    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.

    APPARATUSES AND METHODS FOR SYSTEM INFORMATION (SI) REQUEST THROUGH A CONTENTION-BASED RANDOM ACCESS PROCEDURE

    公开(公告)号:US20190215760A1

    公开(公告)日:2019-07-11

    申请号:US16242262

    申请日:2019-01-08

    Applicant: MEDIATEK INC.

    CPC classification number: H04W48/14 H04W74/0833 H04W80/02 H04W88/023 H04W88/12

    Abstract: A User Equipment (UE) including a wireless transceiver and a controller is provided. The wireless transceiver performs wireless transmission and reception to and from a service network. The controller performs the following during a contention-based random access procedure: transmitting a first message comprising a System Information (SI) request, to the service network via the wireless transceiver; receiving a second message for a contention resolution from the service network via the wireless transceiver in response to transmitting the first message; determining whether the contention resolution is successful according to the second message; and indicating a reception of an acknowledgement for the SI request to an upper layer of a Media Access Control (MAC) layer in response to determining that the contention resolution is successful.

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