Hopfield learning-based and non-linear programming methods for resource allocation in OCDMA networks

Abstract

This study proposes the deployment of the Hopfield neural network (H-NN) approach to optimally assign power in optical code division multiple access (OCDMA) systems. Figures of merit such as the feasibility of solutions and complexity are compared with the classical power allocation methods found in the literature, such as sequential quadratic programming (SQP) and augmented Lagrangian method. The analysed methods are used to solve constrained non-linear optimisation problems in the context of resource allocation for optical networks, especially to deal with energy efficiency in OCDMA networks. The promising performance-complexity trade-off of the modified H-NN is demonstrated through numerical results performed in comparison with classic methods for general problems in non-linear programming. The evaluation is carried out considering challenging OCDMA networks in which different levels of service quality required were considered for large numbers of optical users. The numerical results demonstrated that the three power allocation methods attain suitable convergence for different network sizes, while both the mH-NN and SQP methods achieve suitable equilibrium with less complexity.