Marcele O. K. Mendonça

Marcele O. K. Mendonça, Javier Maroto, Pascal Frossard et al.

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of-the-art DNNs are susceptible to quasi-imperceptible perturbed versions of the original images -- adversarial examples. These perturbations of the network input can lead to disastrous implications in critical areas where wrong decisions can directly affect human lives. Adversarial training is the most efficient solution to defend the network against these malicious attacks. However, adversarial trained networks generally come with lower clean accuracy and higher computational complexity. This work proposes a data selection (DS) strategy to be applied in the mini-batch training. Based on the cross-entropy loss, the most relevant samples in the batch are selected to update the model parameters in the backpropagation. The simulation results show that a good compromise can be obtained regarding robustness and standard accuracy, whereas the computational complexity of the backpropagation pass is reduced.

Marcele O. K. Mendonca, Flor G. Ortiz-Gomez, Jorge Querol et al.

Satellite communications, essential for modern connectivity, extend access to maritime, aeronautical, and remote areas where terrestrial networks are unfeasible. Current GEO systems distribute power and bandwidth uniformly across beams using multi-beam footprints with fractional frequency reuse. However, recent research reveals the limitations of this approach in heterogeneous traffic scenarios, leading to inefficiencies. To address this, this paper presents a machine learning (ML)-based approach to Radio Resource Management (RRM). We treat the RRM task as a regression ML problem, integrating RRM objectives and constraints into the loss function that the ML algorithm aims at minimizing. Moreover, we introduce a context-aware ML metric that evaluates the ML model's performance but also considers the impact of its resource allocation decisions on the overall performance of the communication system.