Estimating Quality of Service on Wi-Fi Stations Using Recurrent Neural Networks

Wireless networks are the most common way to access the Internet, with more than 10 billion Wi-Fi devices already sold. Wireless connections suffer from problems related to spectrum overuse, such as transmission errors and loss of information. Intelligent control systems can be used for network management and to improve Quality of Service (QoS). However, the first step towards such systems is a way to correlate current Wi-Fi readings to a QoS value. This work proposes a model using Recurrent Neural Networks (RNN) to infer such relation, based on real Wi-Fi data, and compares two RNN types: Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). The model predicts four network metrics: throughput, loss, delay, and jitter, based only in traffic data obtained at the AP. The average Root Mean Square Error is of the order of 10 ^-2 for throughput, 10 ^-4 for delay, and 10 ^-5 for jitter and packet loss using both methods.