A hybrid neural network for optimal TDMA transmission scheduling in packet radio networks

In this paper we propose a hybrid method to solve the broadcast scheduling problem in packet radio networks. In the first stage, we use a backtracking sequential coloring algorithm to obtain a minimal TDMA frame length and the corresponding transmission assignments. In the second stage, we employ the noisy chaotic neural network to find the maximum node transmission based on the results obtained in the previous stage. Simulation results show that this hybrid method outperforms previous approaches, such as mean field annealing, a hybrid of the Hopfield neural network and genetic algorithms, the sequential vertex coloring algorithm, and the gradual neural network.