ATM call admission control using sparse distributed memory (II)

Call Admission Control is a key technology of ATM network traffic control. It should be adaptable to the rapid and various changes of the ATM network environment. Conventional approach to the ATM CAC requires network analysis in detail in all cases. The optimal implementation is said to be very difficult. Therefore, neural approach have recently been employed. However, it does not meet the adaptability requirements. It requires additional learning data tables and learning phase during CAC operation We have already proposed a neural network CAC method based on sparse distributed memory (SDM) in previous work. In this paper, we compare it with conventional neural CAC method. The performance of our method is as good as those of the previous neural approaches without additional learning table or learning phases. Our method, however, shows better adaptability to manage changes in ATM network.