{"links":{},"id":2001277,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02001277","sets":["1164:10193:11902:1740725503466"]},"path":["1740725503466"],"owner":"80578","recid":"2001277","title":["無線通信機能配置問題におけるブラックボックス最適化手法の比較評価"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-03-10"},"_buckets":{"deposit":"ff5e9a23-8492-498a-8add-d54b72c659df"},"_deposit":{"id":"2001277","pid":{"type":"depid","value":"2001277","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"無線通信機能配置問題におけるブラックボックス最適化手法の比較評価","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"無線通信機能配置問題におけるブラックボックス最適化手法の比較評価","subitem_title_language":"ja"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"アニーリング","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2025-03-10","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社KDDI総合研究所"},{"subitem_text_value":"株式会社KDDI総合研究所"},{"subitem_text_value":"株式会社KDDI総合研究所"},{"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"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科/慶應義塾大学理工学部物理情報工学科/慶應義塾大学サスティナブル量子AI研究センター/慶應義塾大学ヒト生物学-微生物叢-量子計算研究センター"}]},"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/2001277/files/IPSJ-QS25014005.pdf","label":"IPSJ-QS25014005.pdf"},"date":[{"dateType":"Available","dateValue":"2027-03-10"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-QS25014005.pdf","filesize":[{"value":"1.3 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":"53"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"9a2361a0-7524-4284-8095-f94b73d1f614","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"古澤,瞬"}]},{"creatorNames":[{"creatorName":"道後,千尋"}]},{"creatorNames":[{"creatorName":"斉藤,和広"}]},{"creatorNames":[{"creatorName":"関,優也"}]},{"creatorNames":[{"creatorName":"菊池,脩太"}]},{"creatorNames":[{"creatorName":"田中,宗"}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12894105","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":"2435-6492","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"第5世代移動通信システム(5Gシステム)では,無線アクセスネットワークにおいてサービスごとに仮想的にネットワークを分割するRANスライシング技術により,各種サービスからの多様な品質要件(Quality of Service: QoS)を達成可能になると期待されている.RANスライシングにおいて限られたネットワークリソース下で高い要求品質達成率を実現するため,無線通信機能配置(RFP)を適応的に制御する手法が提案されている.先行研究では,RFP制御問題に対して分散深層強化学習フレームワークであるApe-Xの適用が提案されているが,強化学習手法は一般に事前学習用の訓練データを必要とし,実用面で十分な訓練データの準備が課題となる可能性がある.本研究では,RFP制御問題に対して,異なる二つのブラックボックス最適化手法Factorization Machine with Annealing(FMA)およびBODiを適用し,訓練データ数を変化させたApe-Xとの比較評価により,実用における各手法の使い分けを明らかにした.シミュレーションを用いた評価の結果,要求品質達成率については,事前学習用データが少ない場合においてはFMAが最も優れ,十分な学習データが利用可能な場合においてもFMAがApe-Xと同等以上の結果を示した.一方,実行時間に関しては,Ape-Xはモデルの事前学習に時間は要するものの,次の配置決定までの時間が最も短く,FMAが最も長くなった.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告量子ソフトウェア(QS)"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2025-03-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2025-QS-14"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"created":"2025-02-28T09:14:32.265761+00:00","updated":"2025-02-28T09:14:36.201186+00:00"}