A Multilayer Perceptron Model for Station Grouping in IEEE 802.11ah Networks

Guan Sheng Wang*, Chih Yu Lin, Yu Chee Tseng*, Lan Da Van*

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

With the rapid development of smart devices and wireless communication technologies, IEEE 802.11ah (WiFi HaLow) is designed to solve one of the major problems of Internet of Things (IoT): high collision probability in dense networks. It proposes the Restricted Access Window (RAW) mechanism, where stations (sensors) are partitioned into groups for time-division channel access. The grouping strategy, which highly influences network performance, needs to consider factors including the number of stations per group, and stations' data rates, and locations. With the advance of artificial intelligence technologies, we ponder whether deep learning can help solving this station grouping problem. In this paper, we propose a multilayer perceptron (MLP) model to predict RAW performance. More precisely, the model predicts the corresponding throughputs and packet loss rates of a given set of RAW configurations. Thus, based on the predicted results, we can determine proper RAW parameters. We have validated the proposed method by ns-3 simulations.

原文English
主出版物標題Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
編輯Kemal Akkaya, Olivier Festor, Carol Fung, Mohammad Ashiqur Rahman, Lisandro Zambenedetti Granville, Carlos Raniery Paula dos Santos
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665477161
DOIs
出版狀態Published - 2023
事件36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023 - Miami, United States
持續時間: 8 5月 202312 5月 2023

出版系列

名字Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023

Conference

Conference36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023
國家/地區United States
城市Miami
期間8/05/2312/05/23

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