Presentation 2022-03-27
Reinforcement-learning based selection of CWmin in IEEE 802.11 networks.
Kosuke Sanada,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) This paper proposes Q-learning based selection of contention window minimum value in IEEE 802.11 networks. In the proposed scheme, each node has a learning agent, and learns and searches an optimal selection of contention window minimum value from own transmission attempts. The effectiveness of the proposed scheme is shown from computer simulation results.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) IEEE 802.11 / Reinforcement-learning / Q-learnin / Distribute Coordination Function (DCF)
Paper # CCS2021-51
Date of Issue 2022-03-20 (CCS)

Conference Information
Committee CCS
Conference Date 2022/3/27(1days)
Place (in Japanese) (See Japanese page)
Place (in English) RUSUTSU RESORT HOTEL & CONVENTION
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Tetsuya Asai(Hokkaido Univ.)
Vice Chair Megumi Akai(Hokkaido Univ.) / Masaki Aida(TMU)
Secretary Megumi Akai(TDK) / Masaki Aida(Mie Univ.)
Assistant Hidehiro Nakano(Tokyo City Univ.) / Hiroyasu Ando(Tsukuba Univ.) / Takashi Matsubara(Osaka Univ.) / Sumiko Miyata(Shibaura Inst. of Tech.)

Paper Information
Registration To Technical Committee on Complex Communication Sciences
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Reinforcement-learning based selection of CWmin in IEEE 802.11 networks.
Sub Title (in English)
Keyword(1) IEEE 802.11
Keyword(2) Reinforcement-learning
Keyword(3) Q-learnin
Keyword(4) Distribute Coordination Function (DCF)
1st Author's Name Kosuke Sanada
1st Author's Affiliation Mie University(Mie Univ.)
Date 2022-03-27
Paper # CCS2021-51
Volume (vol) vol.121
Number (no) CCS-442
Page pp.pp.90-95(CCS),
#Pages 6
Date of Issue 2022-03-20 (CCS)