METHOD AND APPARATUS FOR SPATIAL REUSE BASED ON INTERFERENCE RECOGNITION

    公开(公告)号:US20240365245A1

    公开(公告)日:2024-10-31

    申请号:US18329598

    申请日:2023-06-06

    CPC classification number: H04W52/243 H04W52/245 H04W84/12

    Abstract: A spatial reuse (SR) method in a wireless LAN (WLAN) system may be provided.
    The operating method according to an embodiment of the present disclosure may include: the SR system according to an embodiment of the present disclosure may include: detecting a new signal by receiving communication environment information, comparing strength of the new signal with a first threshold, determining whether the new signal is a target signal of the WLAN system based on the determination that the strength of the new signal exceeds the first threshold, determining whether the new signal is a negligible signal based on the determination that the new signal is not a target signal of the WLAN system and performing spatial reuse simultaneous transmission by adjusting transmission power based on the determination that the new signal is the negligible signal.

    APPARATUS AND METHOD OF SYNTHETIC DATA GENERATION USING ENVIRONMENTAL MODELS

    公开(公告)号:US20240095427A1

    公开(公告)日:2024-03-21

    申请号:US18083612

    申请日:2022-12-19

    CPC classification number: G06F30/27

    Abstract: Disclosed are a method and an apparatus of synthetic data generation for training an artificial intelligence model. The method includes: reading a target environmental model simulating a target environment to generate synthetic data among a plurality of environmental models simulating a plurality of actual environments, respectively; extracting an environmental feature which influences generation of data in the target environment; configuring a synthetic data generation function of a synthetic data generation simulator for the target environmental model so that the synthetic data reflects the environmental feature; and generating the synthetic data by using the synthetic data generation simulator for the target environmental model, and has an effect of being capable of generating high-quality learning synthetic data which may be used for training an artificial intelligence model.

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