A machine learning approach for dynamic control of RTS/CTS in WLANs

In this paper, we proposed a novel algorithm to dynamically enable and disable IEEE 802.11 DCF's RTS/CTS handshake. We start by conducting an experimental characterization of the performance of RTS/CTS as a function of packet size, transmission rate, and network contention, which complements existing work that evaluated RTS/CTS performance analytically and empirically. Motivated by our experimental evaluation of RTS/CTS performance, our algorithm uses current packet size and transmission rate, as well as an estimate of network contention to dynamically decide whether to use RTS/CTS or not. To the best of our knowledge, the proposed algorithm is the first to enable and disable the RTS/CTS handshake based on a set of current network conditions, and automatically adapt as these conditions change. Simulation results using a variety of WLAN scenarios, including synthetic and real traffic traces, demonstrate that the proposed approach consistently outperforms current best practices, such as never enabling RTS/CTS or setting the RTS Threshold (RT), which is used to decide whether to switch RTS/CTS on or off, to a static value.