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This paper proposes the deployment of the Hopfield's artificial neuralnetwork (H-NN) approach to optimally assign power in optical code divisionmultiple access (OCDMA) systems. Figures of merit such as feasibility ofsolutions and complexity are compared with the classical power allocationmethods found in the literature, such as Sequential Quadratic Programming (SQP)and Augmented Lagrangian Method (ALM). The analyzed methods are used to solveconstrained nonlinear optimization problems in the context of resourceallocation for optical networks, specially to deal with the energy efficiency(EE) in OCDMA networks. The promising performance-complexity tradeoff of themodified H-NN is demonstrated through numerical results performed in comparisonwith classic methods for general problems in nonlinear programming. Theevaluation is carried out considering challenging OCDMA networks in whichdifferent levels of QoS were considered for large numbers of optical users.

Hopfield Learning-based and Nonlinear Programming methods for Resource Allocation in OCDMA Networks