The Application of Hopfield Neural Network Techniques to Problems of Routing and Scheduling in Packet Radio Networks

Although the issues of routing and scheduling in packet radio networks are highly interdependent, few studies have addressed their interactions. In this report, we review the major issues associated with the joint study of these problems, and we address the problem of routing for the minimization of congestion as a first step toward the solution of the joint routing-scheduling problem. We formulate this as a combinatorial optimization problem, and we develop a Hopfield neural network NN model for its solution. The issues associated with the development of the Hopfield NN model for this problem are discussed in detail. In particular, the determination of the coefficients in the connection weights is the most critical issue in the design and simulation of Hopfield NN models. In our studies, we use the method of Lagrange multipliers, which permits these coefficients to vary dynamically along with the evolution of the system state. Extensive software simulation results demonstrate the ability of our approach to determine good sets of routes in large, heavily-congested networks. RH