Abstract
The antenna design is a challenging task, which might be time-consuming using conventional computational methods that typically require high computational capability, due to the need for several sweeps and re-running processes. This work proposes an efficient and accurate computational intelligence-based methodology for the antenna design and optimization. The computational technical solution consists of a surrogate model application, composed of a Multilayer Perceptron (MLP) artificial neural network with backpropagation for the regression process. Combined with the surrogate model, two multiobjective optimization meta-heuristic strategies, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), are used to overcome the mentioned issues from the traditional antenna design method. A study of case considering a dipole antenna for the 3.5 GHz 5G band is reported, as proof of the proposed methodology concept. Comparisons of antenna impedance matching obtained by the proposed methodology, numerical full-wave results from ANSYS HFSS and experimental result from the antenna prototype are performed for demonstrating its applicability and effectiveness for antenna development.
Highlights
Antennas are essential wireless communication components, such as radar, cellular systems, satellite communications, RFID tags, and airborne navigation
This procedure is named Learning-by-examples (LBE) technique, which is a computer-aided approach based on machine learning that is focused on solving complex real-world problems, which are mapped by a surrogate model (SM) [10]
This work proposed a computational intelligence-based methodology for antenna development. it was performed an in-depth explanation of the methodology and provided a step-by-step to build the dataset, surrogate model, and optimization
Summary
Antennas are essential wireless communication components, such as radar, cellular systems, satellite communications, RFID tags, and airborne navigation. The evolution in the computational area from the last decades provides a revolutionary and efficient set of tools, called artificial intelligence (AI), including machine learning (ML) These AI-based models have been applied to improve the quality and accuracy of engineering applications over different knowledge areas. Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS ducing and motivating RF-engineers to discover, in computer science, suitable skills to emulate the behavior of EM-solvers systems based on a set of collected examples used to train computational models [10] This procedure is named Learning-by-examples (LBE) technique, which is a computer-aided approach based on machine learning that is focused on solving complex real-world problems, which are mapped by a surrogate model (SM) [10].
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