{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210126","sets":["1164:3925:10503:10504"]},"path":["10504"],"owner":"44499","recid":"210126","title":["密集無線LAN環境におけるQ学習を用いた送信電力・信号検知閾値制御の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-08"},"_buckets":{"deposit":"756aac30-3785-4b14-a9a7-8a0a508c0f5e"},"_deposit":{"id":"210126","pid":{"type":"depid","value":"210126","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"密集無線LAN環境におけるQ学習を用いた送信電力・信号検知閾値制御の検討","author_link":["531293","531295","531294"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"密集無線LAN環境におけるQ学習を用いた送信電力・信号検知閾値制御の検討"},{"subitem_title":"A Study of Power Control and Dynamic Sensitivity Control using Q-Learning in dense Wireless LAN","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"無線ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-03-08","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"慶應義塾大学理工学部情報工学科"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Science and Technology, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Technology, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Technology, Keio University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/210126/files/IPSJ-CSEC21092029.pdf","label":"IPSJ-CSEC21092029.pdf"},"date":[{"dateType":"Available","dateValue":"2023-03-08"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSEC21092029.pdf","filesize":[{"value":"1.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4456470b-b0d6-44ad-ba96-84f5b4864ead","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"武松, 未来"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"坂井, 渉太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"重野, 寛"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11235941","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8655","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"無線 LAN の密集化によるスループット性能の低下を解決するために送信電力・信号検知閾値制御を初めとする周波数資源を有効活用する空間再利用に関する技術の採用が検討されている．既存研究 RTOT アルゴリズムでは，1 つの変数 M により送信電力と信号検知閾値を同時にコントロールすることが可能となった．しかし，シナリオに応じた適切な変数 M を使用する必要があるが，既存研究では変数 M の決定に関しては考慮していない．そこで本稿では，Q 学習により RTOT アルゴリズムの変数 M を決定することを提案する．また，Q 学習についての既存研究で多く使用されている報酬の計算方法では公平性を考慮していないものが多く，スループットは高いが，公平性が低いという問題があったため，本稿では，公平性を向上させる報酬の計算方法を提案する．シミュレーションによって，提案報酬を使用した Q 学習を用いた RTOT アルゴリズムでは，既存手法に比べてスループットと公平性ともに向上することを確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータセキュリティ（CSEC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"29","bibliographicVolumeNumber":"2021-CSEC-92"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:15:46.079439+00:00","created":"2025-01-19T01:11:23.557005+00:00","links":{},"id":210126}