Design and analysis of an active predictive algorithm in wireless multicast networks

With the ever-increasing wireless multicast data applications recently, considerable efforts have focused on the large scale heterogeneous wireless multicast, especially those with large propagation delays, which means the feedbacks arriving at the source node are somewhat outdated and harmful to the control actions. To attack the above problem, this paper describes a novel, autonomous, and predictive wireless multicast flow control scheme, the so-called proportional, integrative plus neural network (PINN) predictive technique, which includes two components: the PI flow controller located at the wireless multicast source has explicit rate algorithm to regulate the transmission rate; and the neural network part located at the middle branch node predicts the available buffer occupancy for those longer delay receivers. The ultimate sending rate of the multicast source is the expected receiving rates computed by PI controller based on the consolidated feedback information, and it can be accommodated by its participating branches. This network-assisted property is different from the existing control schemes in that neural network controller can predict the buffer occupancy caused by those long delay receivers, which probably cause irresponsiveness of a wireless multicast flow. This active scheme makes the control more responsive to the network status, therefore, the rate adaptation can be in a timely manner for the sender to react to network congestion quick. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support wireless multi-rate multicast transmission based on feedback of explicit rates.