Performance-Constrained Multi-Objective Optimization of Antennas for Miniaturization Design

Author:

Yang Qi1,Wang Hongqiang1,Peng Xin2

Affiliation:

1. National University of Defense Technology

2. Beijing University of Posts and Telecommunications

Abstract

Abstract

The miniaturization of antennas is crucial as it improves the integration of wireless communication system. In order to achieve miniaturization of antennas, a performance-constrained multi-objective optimization method (PCMOM) considering the size and return loss is proposed. In the PCMOM method, a topology optimization strategy based on a multi-port network model is introduced, enabling the formation of antennas with various topological structures. Furthermore, we integrate the constraint of return loss performance into the non-dominated sorting genetic algorithm II (NSGA-II), eliminating solutions that do not meet performance requirements. Three patch antennas are designed using the PCMOM method and two of them are fabricated. Experimental results demonstrate that the proposed PCMOM method can effectively address the complex trade-off issues in antenna miniaturization design.

Publisher

Springer Science and Business Media LLC

Reference18 articles.

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3. A fast and elitist multiobjective genetic algorithm: NSGA-II;Deb K;IEEE Transactions on Evolutionary Computation,2002

4. Miniaturization of a circular patch microstrip antenna using an arc projection;Motevasselian M;IEEE Antennas and Wireless Propagation Letters,2016

5. Miniaturization of monopole antenna based on spoof surface plasmon polaritons;Qu S;IEEE Antennas and Wireless Propagation Letters,2021

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