A data-driven approach to wireless channel available throughput estimation and prediction - Yao XIAO; Junshuo LIU; Zhifu LONG; Caiming QIU (物联网学报)
The rapid development of wireless local area network technology has brought about new challenges that significantly affect the communication quality of wireless channels.Wireless channel quality is crucial for guiding routers in managing sudden congestion and selecting appropriate channels.A set of solutions using channel available throughput as an indicator was designed.Firstly, invasive data collection methods were used to collect channel data, and an artifical neural network was trained to estimate the available throughput of the channel at the current time.Subsequently, non-invasive data collection methods were utilized to collect channel data, and an improved recurrent neural network model was employed to predict the available throughput of the channel for a future period.Experiments on the real data show that the scheme can effectively estimate and predict the available throughput of the channel, providing guidance for router decisions.