A Deep Probabilistic Control Machinery for Auto-Configuration of WiFi Link Parameters

Abstract

IEEE 802.11ac high throughput extension for wireless local area network comes with a large number of link layer configuration parameters, such as 4 different channel bonding levels, 10 different modulation and coding schemes, frame aggregation setup etc. However, the optimal combination of link configuration parameters, which maximizes the link layer performance, depends on the perceived channel quality based on the signal strength, channel noise and external interference. Considering the highly dynamic, nonlinear and time-varying nature of wireless channel quality, a dynamic adaptation of link configuration parameters gives a stable and optimized link layer performance. Nevertheless, the existing literature fails to design a robust mechanism for handling all the parameters simultaneously. In this article, we develop a control theoretic approach governed by a deep probabilistic machinery to design a robust and scalable dynamic link parameter adaptation mechanism. We apply deep neural network based Gaussian process regression to predict the link layer throughput and model predictive control based approach to find out the link configuration parameter that optimizes the overall link layer performance. The proposed mechanism is implemented and tested over a testbed setup, and we observe that it can significantly boost up the link layer performance compared to various baseline mechanisms.