Wael Elshennawy | Searcharxiv

arXiv2026

MLOps-Assisted Anomalous Reflector Metasurfaces Design Based on Red Hat OpenShift AI

The integration of artificial intelligence as a design tool for metasurfaces, and the implementation of a deep-learning model pose a challenge in the development of an automated solution due to high resources requirements. The presented work introduces a network-layer solution to configure such environment for end user objectives, and for an underlying physical-layer technology. An architecture is developed to design an anomalous reflector by employing the Redhat Openshift AI (RHOAI) technology to support an automated machine learning operations (MLOps) framework in smart radio environments. This entails the design of lossless impenetrable metasurfaces characterized by a scalar surface impedance for an optimal anomalous reflection, achieved by optimizing the number of the Floquet modes through the utilization of a local power conservation constraint qualified as a fitness function. The metasurfaces design process is implemented by using a conditional generative adversarial network (cGAN). An extended cGAN with a surrogate model assists in a high-quality freeform metasurfaces design, where it introduces a swift simulation tool for the metasurfaces design process and analysis of the far-field model. The paper focuses on the challenges of building such a system, and potential abstraction layers. The training accuracy value of the proposed model demonstrates the feasibility and benefits of deploying in containerized environment of Red Hat Openshift in comparison with other deployments of ResNet-50 reported in literature.

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arXiv2023

Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks

The paper investigates the direction-of-arrival (DOA) estimation of narrow band signals with conventional co-prime arrays by using probabilistic Bayesian neural networks (PBNN). A super resolution DOA estimation method based on Bayesian neural networks and a spatially overcomplete array output formulation overcomes the pre-assumption dependencies of the model-driven DOA estimation methods. The proposed DOA estimation method utilizes a PBNN model to capture both data and model uncertainty. The developed PBNN model is trained to do the mapping from the pseudo-spectrum to the super resolution spectrum. This learning-based method enhances the generalization of untrained scenarios, and it provides robustness to non-ideal conditions, e.g., small angle separation, data scarcity, and imperfect arrays, etc. Simulation results demonstrate the loss curves of the PBNN model and deterministic model. Simulations are carried out to validate the performance of PBNN model compared to a deterministic model of conventional neural networks (CNN).

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