Learning automata-based receiver conflict avoidance algorithms for WDM broadcast-and-select star networks

A new receiver conflict avoidance algorithm for wavelength-division multiplexing (WDM) broadcast-and-select star networks is introduced. The proposed algorithm is based on the use of learning automata in order to reduce the number of receiver conflicts and, consequently, improve the performance of the network. According to the proposed scheme, each node of the network is provided with a learning automaton; the learning automaton decides which of the packets waiting for transmission will be transmitted at the beginning of the next time slot. The asymptotic behavior of the system, which consists of the automata and the network, is analyzed and it is proved that the probability of choosing each packet asymptotically tends to be proportional to the probability that no receiver conflict will appear at the destination node of this packet. Furthermore, extensive simulation results are presented, which indicate that significant performance improvement is achieved when the proposed algorithm is applied on the basic DT-WDMA protocol.