TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks

TCP Vegas performance can be improved since its rate-based congestion control mechanism could proactively avoid possible congestion and packet losses in multi-hop ad hoc networks. Nevertheless, Vegas cannot make full advantage of available bandwidth to transmit packets since incorrect bandwidth estimates may occur due to frequent topology changes caused by node mobility. This paper proposes an improved TCP Vegas based on the grey prediction theory, named TCP-Gvegas, for multi-hop ad hoc networks, which has the capability of prediction and self-adaption, as well as three enhanced aspects in the phase of congestion avoidance. The lower layers’ parameters are considered in the throughput model to improve the accuracy of theoretical throughput. The prediction of future throughput based on grey prediction is used to promote the online control. The optimal exploration method based on Q-Learning and Round Trip Time quantizer are applied to search for the more reasonable changing size of congestion window. Besides, the convergence analysis of grey prediction by using the Lyapunov’s second method proves that a shorter input data length of prediction implies a faster convergence rate. The simulation results show that the TCP-Gvegas achieves a substantially higher throughput and lower delay than Vegas in multi-hop ad hoc networks.