2026-10-07T10:23:07Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33979594 2026-09-24T04:35:49Z category_1069 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_PARISO: pixelated antennas and reconfigurable intelligent surface optimizer.zip Sekhar Sekharan (25101997) Reza Fazel-Rezai (4019774) Sima Noghanian (25102000) Antennas and Propagation genetic algorithms infinite arrays miniaturization optimizations periodic structures pixelation reconfigurable intelligent surfaces <p>The design and optimization of large antenna arrays and Reconfigurable Intelligent Surfaces (RIS) have become critical for emerging wireless communication systems, including 5G and 6G networks. Although technologies such as Massive Multiple Input Multiple Output (MIMO) and RIS offer unprecedented opportunities for enhanced coverage, improved spectral efficiency, and adaptive propagation environments, the corresponding electromagnetic structures often present extremely large and complex design spaces that require exhaustive exploration to achieve optimal performance. As a result, achieving goals such as miniaturization, bandwidth enhancement, improved scattering characteristics, or precise phase control becomes a major focus when working with these complex spaces. In this paper, we introduce a systematic methodology for developing an optimization framework dedicated to planar electromagnetic structures using binary pixelization of the conducting surface. By discretizing the design region into a matrix of metallic and non-metallic pixels, the approach enables flexible topology manipulation while maintaining compatibility with full-wave simulation tools. We then couple this representation with heuristic optimization methods, specifically Genetic Algorithms (GA) and Surrogate-Assisted Differential Evolution for Antenna (SADEA) optimization approaches, to efficiently navigate the high-dimensional search space and identify high-performance designs aligned with target specifications. This paper describes the implementation of this workflow in MATLAB, detailing the generation of pixelized geometries, their integration into simulation routines, the formulation of fitness functions, and their integration with the optimization tools. The complete MATLAB codes, along with supporting user interface and examples, are made publicly available via GitHub to facilitate reproducibility and encourage further research in computational design of advanced planar structures.</p> 2026-09-24T04:35:49Z Dataset Dataset 10.3389/fanpr.2026.1830853.s002 https://figshare.com/articles/dataset/Data_Sheet_1_PARISO_pixelated_antennas_and_reconfigurable_intelligent_surface_optimizer_zip/33979594 CC BY 4.0