カテゴリ: 研究会(論文単位)論文No: CMN24021グループ名: 【C】電子・情報・システム部門 通信研究会発行日: 2024/03/25タイトル(英語): Indoor Localization Method Based on the Analyses of Surrounding Wi-Fi Access Points Using ChatGPT著者名: Rahmadya Budi (Andalas University),Takeda Shigeki(Ibaraki University),Sun Ran(Ibaraki University),Song Zequn(Ibaraki University),Kumara Hadi Danang(Ibaraki University)著者名(英語): Budi Rahmadya(Andalas University),Shigeki Takeda(Ibaraki University),Ran Sun(Ibaraki University),Zequn Song(Ibaraki University),Danang Kumara Hadi(Ibaraki University)キーワード: Indoor localization|Wi-Fi|ChatGPT|RTLS|AI/ML|ISAC|Indoor localization|Wi-Fi|ChatGPT|RTLS|AI/ML|ISAC要約(日本語): Argent demands on indoor real-time localization systems (RTLS) are accelerating research and developments on these technologies. Recent advances in Wi-Fi technologies, including Wi-Fi 6 and 7, further facilitate the development of RTLS technologies by utilizing wireless communications and artificial intelligence/machine learning (AI/ML) technologies. These research and development activities also accelerate future wireless communication for achieving the integrated sensing and communication (ISAC) concept. The recent developments in Wi-Fi technologies have led to increased numbers of APs for gaining reliable wireless links. Therefore, we observe a large number of SSIDs in workplaces, office environments, and homes. These situations enable exploiting observed Wi-Fi information for localizations. This paper proposed an indoor localization method based on analyzing surrounding Wi-Fi access points using ChatGPT. The experiments validated that ChatGPT provided corresponding room names to the given unknown Wi-Fi logs, based on analyzing Wi-Fi logs observed in priori in individual rooms.要約(英語): Argent demands on indoor real-time localization systems (RTLS) are accelerating research and developments on these technologies. Recent advances in Wi-Fi technologies, including Wi-Fi 6 and 7, further facilitate the development of RTLS technologies by utilizing wireless communications and artificial intelligence/machine learning (AI/ML) technologies. These research and development activities also accelerate future wireless communication for achieving the integrated sensing and communication (ISAC) concept. The recent developments in Wi-Fi technologies have led to increased numbers of APs for gaining reliable wireless links. Therefore, we observe a large number of SSIDs in workplaces, office environments, and homes. These situations enable exploiting observed Wi-Fi information for localizations. This paper proposed an indoor localization method based on analyzing surrounding Wi-Fi access points using ChatGPT. The experiments validated that ChatGPT provided corresponding room names to the given unknown Wi-Fi logs, based on analyzing Wi-Fi logs observed in priori in individual rooms.本誌: 2024年3月28日-2024年3月29日通信研究会本誌掲載ページ: 25-30 p原稿種別: 英語PDFファイルサイズ: 1,134 Kバイト
