最尤性を保ちつつ雑音候補のリストサイズ増加を抑制したSGRANDの改良法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/12/01

タイトル(英語): Proposal of SGRAND Algorithm with Less List Size of Noise Pattern Candidate Growth while Keeping ML Property

著者名: 熊本　多良（名城大学 大学院 理工学研究科），宇佐見　庄五（名城大学 大学院 理工学研究科），高比良　宗一（名城大学 大学院 理工学研究科）

著者名(英語): Taro Kumamoto (Graduate School of Science and Engineering, Meijo University), Shogo Usami (Graduate School of Science and Engineering, Meijo University), Souichi Takahira (Graduate School of Science and Engineering, Meijo University)

キーワード: GRAND，SGRAND，最尤復号　　GRAND，SGRAND，ML-Decoding

要約(英語): To achieve ultra-high reliability and ultra-low latency required for Beyond 5G or 6G, low error rate decoding method is needed with short code lengths and high code rates. Additionally, a universal decoding method that can be applied to any code which appears in the future be desirable. The maximum likelihood decoding is the best decoding method in information theory, but it is considered difficult to implement due to its huge computational requirements. Recently, Soft GRAND (SGRAND) has been proposed as an effective decoding method for any high-rate, short code length block code in any continuous channel. This method obtains the same decoding result as the maximum likelihood decoding by checking the most likely noise sequantially. However, SGRAND has a problem in which the size of the noise candidate list held internally increases linearly with each noise check. This paper proposes a method to reduce the size of the candidate list in SGRAND, while maintaining the same decoding results as maximum likelihood decoding, by modifying the noise candidate generation algorithm in SGRAND.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.12 （2023） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1083-1089 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/12/143_1083/_article/-char/ja/