Self-optimizing window flow control in high speed data networks

The problem of selecting the optimum window size in high-speed data networks is considered. A self-optimizing method is proposed to adapt the window size to network conditions. The scheme employs a cross-correlation technique for process identification with a random perturbation signal. Simulation results are presented for the isarithmic flow control to show the convergence rate and stability characteristics of the self-optimizing scheme