Research Article
Failure Reasons Identification for the Next Generation WLAN: A Machine Learning Approach
@INPROCEEDINGS{10.1007/978-3-030-44751-9_35, author={Zhaozhe Jiang and Bo Li and Mao Yang and Zhongjiang Yan and Qi Yang}, title={Failure Reasons Identification for the Next Generation WLAN: A Machine Learning Approach}, proceedings={IoT as a Service. 5th EAI International Conference, IoTaaS 2019, Xi’an, China, November 16-17, 2019, Proceedings}, proceedings_a={IOTAAS}, year={2020}, month={6}, keywords={Machine learning Failure reasons Access environment state}, doi={10.1007/978-3-030-44751-9_35} }- Zhaozhe Jiang
Bo Li
Mao Yang
Zhongjiang Yan
Qi Yang
Year: 2020
Failure Reasons Identification for the Next Generation WLAN: A Machine Learning Approach
IOTAAS
Springer
DOI: 10.1007/978-3-030-44751-9_35
Abstract
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%.