TY - GEN
T1 - Failure Reasons Identification for the Next Generation WLAN
T2 - 5th EAI International Conference on IoT as a Service, IoTaaS 2019
AU - Jiang, Zhaozhe
AU - Li, Bo
AU - Yang, Mao
AU - Yan, Zhongjiang
AU - Yang, Qi
N1 - Publisher Copyright:
© 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2020
Y1 - 2020
N2 - Artificial Intelligence (AI) is one of the hottest research directions nowadays. Machine learning is an important branch of AI. It allows the machine to make its own decisions without human telling the computer exactly what to do. At the same time, Media Access Control (MAC) is also an important technology for the next generation Wireless Local Area Network (WLAN). However, due to transmission collision, noise, interference, channel fading and other reasons, the transmission between access point (AP) and station (STA) may fail. This is limiting the overall performance. If the node can obtain the real-time failure reasons, it can adjust protocol parameters accordingly such as Modulation and Coding Scheme (MCS) and Contention Window (CW). Then, the overall performance of WLAN is improved. Therefore, a machine learning based failure reason identification approach is proposed for the next generation WLAN. In this paper, access environment is divided into four categories: nice, severe collision, deep fading and both deep fading. Different training models are used to train the data. Through our experiments, the accuracy can reach 83%, while that of Random Forest model can reach 99%.
AB - Artificial Intelligence (AI) is one of the hottest research directions nowadays. Machine learning is an important branch of AI. It allows the machine to make its own decisions without human telling the computer exactly what to do. At the same time, Media Access Control (MAC) is also an important technology for the next generation Wireless Local Area Network (WLAN). However, due to transmission collision, noise, interference, channel fading and other reasons, the transmission between access point (AP) and station (STA) may fail. This is limiting the overall performance. If the node can obtain the real-time failure reasons, it can adjust protocol parameters accordingly such as Modulation and Coding Scheme (MCS) and Contention Window (CW). Then, the overall performance of WLAN is improved. Therefore, a machine learning based failure reason identification approach is proposed for the next generation WLAN. In this paper, access environment is divided into four categories: nice, severe collision, deep fading and both deep fading. Different training models are used to train the data. Through our experiments, the accuracy can reach 83%, while that of Random Forest model can reach 99%.
KW - Access environment state
KW - Failure reasons
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85084001851&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-44751-9_35
DO - 10.1007/978-3-030-44751-9_35
M3 - 会议稿件
AN - SCOPUS:85084001851
SN - 9783030447502
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 417
EP - 426
BT - IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings
A2 - Li, Bo
A2 - Yang, Mao
A2 - Yan, Zhongjiang
A2 - Zheng, Jie
A2 - Fang, Yong
PB - Springer
Y2 - 16 November 2019 through 17 November 2019
ER -