TY - GEN
T1 - A Multilayer Perceptron Model for Station Grouping in IEEE 802.11ah Networks
AU - Wang, Guan Sheng
AU - Lin, Chih Yu
AU - Tseng, Yu Chee
AU - Van, Lan Da
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - IEEE 802.11ah
KW - multilayer perceptron
KW - restricted access window (RAW)
KW - station grouping
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85164719584&partnerID=8YFLogxK
U2 - 10.1109/NOMS56928.2023.10154425
DO - 10.1109/NOMS56928.2023.10154425
M3 - Conference contribution
AN - SCOPUS:85164719584
T3 - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
BT - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
A2 - Akkaya, Kemal
A2 - Festor, Olivier
A2 - Fung, Carol
A2 - Rahman, Mohammad Ashiqur
A2 - Granville, Lisandro Zambenedetti
A2 - dos Santos, Carlos Raniery Paula
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023
Y2 - 8 May 2023 through 12 May 2023
ER -