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

TL;DR

This paper explores the use of Hopfield neural networks for power allocation in OCDMA networks, comparing its efficiency and solution quality with classical nonlinear programming methods in complex, QoS-sensitive scenarios.

Contribution

It introduces a Hopfield neural network approach for resource allocation in OCDMA networks and demonstrates its promising performance and complexity advantages over traditional methods.

Findings

H-NN achieves a good tradeoff between performance and complexity.

H-NN outperforms classical methods in large, QoS-sensitive OCDMA networks.

Numerical results validate the effectiveness of the proposed approach.

Abstract

This paper proposes the deployment of the Hopfield's artificial neural network (H-NN) approach to optimally assign power in optical code division multiple access (OCDMA) systems. Figures of merit such as 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 (ALM). The analyzed methods are used to solve constrained nonlinear optimization problems in the context of resource allocation for optical networks, specially to deal with the energy efficiency (EE) in OCDMA networks. The promising performance-complexity tradeoff of the modified H-NN is demonstrated through numerical results performed in comparison with classic methods for general problems in nonlinear programming. The evaluation is carried out considering challenging OCDMA networks in which different levels of QoS were considered for large numbers of optical users.