In this post we present the work fully available under this link: ML-Optimized Beam-based Radio Coverage Processing in IEEE 802.11 WLAN Networks, about radio coverage processing in wlan and its optimization using machine learning (ML).
Dynamic Radio Resource Management (RRM) is a major building block of Wireless LAN Controllers (WLC) function in WLAN networks. In a dense and frequently changing WLANs, it maximizes Wireless Devices (WD) opportunity to transmit and guarantees conformance to the design Service Level Agreement (SLA).
To achieve this performance, a WLC processes and applies a network-wide optimized radio plan based on data from access points (AP) and upper-layer application services. The next figure shows an example of unified wlan architecture aimed at supporting RRM operation and processings.
This coverage processing requires a “realistic” modelization approach of the radio environment and a quick adaptation to frequent changes. In this paper, the author builds on a Beam-based approach to radio coverage modelization. The work proposes a new Machine Learning Regression (MLR)-based optimization and compare it to the NURBS-based solution performance, as an out of path alternative: instead of analytically calculate the coverage we rely on the environmental variables to predict them…
