V2X SERVICES FOR PROVIDING JOURNEY-SPECIFIC QOS PREDICTIONS

    公开(公告)号:US20230074288A1

    公开(公告)日:2023-03-09

    申请号:US17437553

    申请日:2020-05-06

    Abstract: Disclosed embodiments are related to techniques for implementing Vehicle-to-Everything (V2X) communications in Multi-access Edge Computing (MEC) systems and networks. V2X system scenarios characterized by high mobility and dynamic topologies, where the accuracy and timeliness of radio network information, location information may be hampered by environmental conditions and deployment density of network infrastructure. The disclosed embodiments provide a V2X Information Service (VIS) framework for cooperative acquisition, partitioning, and distribution of information for efficient, journey-specific quality-of-service (QoS) predictions. The VIS framework identifies space/time correlations between radio condition/quality data collected in V2X system(s) and a vehicle's planned journey for better prediction of the radio conditions/quality of the communication network along the designated route. As a consequence, the VIS may expose journey-specific information about the QoS prediction to authorized devices. Other embodiments may be described and/or claimed.

    ENHANCED ON-THE-GO ARTIFICIAL INTELLIGENCE FOR WIRELESS DEVICES

    公开(公告)号:US20240298194A1

    公开(公告)日:2024-09-05

    申请号:US18575792

    申请日:2022-08-09

    CPC classification number: H04W24/02 H04W48/18 H04W64/00

    Abstract: This disclosure describes systems, methods, and devices related to facilitating machine learning-based operations at a User Equipment (UE) connected to a radio access network (RAN). A network AI/ML (artificial intelligence/machine learning) service or function may identify a first request, received from a user equipment (UE) device, for a machine learning model configuration; determine a location of the UE device; select, based on the first request and the location, an available machine learning agent; format a second request to the available machine learning agent for the machine learning configuration; identify the machine learning configuration received from the available machine learning agent based on the second request; and format a response to the first request, the response comprising the machine learning configuration for the UE device.

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