A neural network approach to routing without interference in multihop radio networks

The issues of routing and scheduling the activation of links in packet radio networks are highly interdependent. In this paper, we consider a form of the problem of routing for the minimization of congestion as a step toward the study of the joint routing-scheduling problem. We formulate this as a combinatorial-optimization problem, and we use Hopfield neural networks (NN) for its solution. 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 capability of our approach to determine good sets of routes in large heavily congested networks.