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Mobile and wireless systems beyond 3G are being designed under the user-centric paradigm. Dynamic Resource Allocation (DRA) is a topic on intensive research to address efficiently such paradigm. Hopfield Neural Networks (HNN) have proved useful in the past to solve this kind of complex optimization problems. Recently, various approaches have been proposed to realize HNN-based user-centric DRA. However, the initial algorithms suffer from severe instability problems impacting the overall performance. This paper analyzes the source of the existing limitations and proposes an enhanced formulation, ensuring maximum resource utilization while optimizing the convergence of the neural network.
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