Learning-TCP: A Novel Learning Automata Based Congestion Window Updating Mechanism for Ad hoc Wireless Networks

Abstract

The use of traditional TCP, in its present form, for reliable transport over Ad hoc Wireless Networks (AWNs) leads to a significant degradation in the network performance. This is primarily due to the congestion window (cwnd) updation and congestion control mechanisms employed by TCP and its inability to distinguish congestion losses from wireless losses. In order to provide an efficient reliable transport over AWNs, we propose Learning-TCP, a novel learning automata based reliable transport protocol, which efficiently adjusts the cwnd size and thus reduces the packet losses. The key idea behind Learning-TCP is that, it dynamically adapts to the changing network conditions and appropriately updates the cwnd size by observing the arrival of acknowledgment (ACK) and duplicate ACK (DUPACK) packets. Learning-TCP, unlike other existing proposals for reliable transport over AWNs, does not require any explicit feedback, such as congestion and link failure notifications, from the network. We provide extensive simulation studies of Learning-TCP under varying network conditions, that show increased throughput (9-18%) and reduced packet loss (42-55%) compared to that of TCP.