An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks

In an enterprise Wifi network, indoor and dense, co-channel interference is a major issue. Wifi controllers help tackle this problem thanks to radio resource management (RRM). RRM is a fundamental building block of any controller functional architecture.

One aim of RRM is to process the radio plan such as to maximize the overall network transmit opportunity.

In this work, we present our dynamic RRM (dRRM), WLCx, solution in contrast to other research and vendors’ solutions. We build our solution model on a novel per-beam coverage representation approach.

The idea of WLCx is to allow more control over the architecture design aspects and recommendations. This dynamization of RRM comes at a price in terms of time and resources consumption.

To improve the scalability of our solution, we have introduced a Machine Learning (ML)-based optimization.

Our ML-optimized dRRM solution, M-WLCx, achieves almost 79.77% time reduction in comparison with the basic WLCx solution.


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