WDM passive star networks: receiver collisions avoidance algorithms using multifeedback learning automata

Abstract

A receiver collision avoidance algorithm for WDM broadcast-and-select star networks is introduced. It is based on the use of learning automata to reduce the number of receiver collisions and, consequently, to improve the performance of the network. Each station has a learning automaton that decides which of the packets waiting for transmission will be transmitted at the beginning of the next time slot. The learning automaton used is a multifeedback automaton, specially designed for the receiver collision avoidance problem of WDM broadcast-and-select star networks. The asymptotic behavior of the system, which consists of the automata and the network, is analyzed. The probability of choosing each packet asymptotically tends to be proportional to the probability that no receiver collision will appear at the destination node of this packet. Extensive simulation results indicate that a significant performance improvement can be achieved when the algorithm is applied on the basic DT-WDMA protocol.<>