TCP-RLLD: TCP with reinforcement learning based loss differentiation for mobile adhoc networks

TCP-Transmission Control Protocol provides connection oriented and reliable communication. TCP’s default consideration of any loss as a cause of network congestion degrades the end-to-end performance in wireless networks specially in MANETs-Mobile Adhoc Networks. TCP should identify various non congestion losses such as channel loss and route failure loss to act accordingly. Over the years, researchers have proposed Machine Learning based network protocols for accurate and efficient decision making. Reinforcement learning suits better for the dynamic networks with unpredictable traffic and topology. TCP-RLLD, TCP with Reinforcement Learning based Loss Differentiation is an end-to-end transport layer solution to predict cause of a packet loss. TCP’s default consideration of any loss as a congestion loss is overruled by TCP-RLLD to avoid unnecessary reduction of the transmission rate. TCP-RLLD is evaluated with multiple TCP variants for the Mobile Adhoc Networks. The extensive evaluation is performed with NS-3 simulator. This paper discusses TCP-RLLD architecture along with the detail of performance improvement.