Control of Mutual Coupling in Microstrip Antenna Design Based on Bio-inspired Algorithms

Abstract This paper presents a model that uses bio-inspired optimization algorithms to control of the mutual coupling (MC) in Multiple-Input Multiple-Output (MIMO) systems. The method promotes an increase in the channel throughput and a reduction in the distance among the array elements, searching values less than a half wavelength. A microstrip antenna design, at the frequency of 26 GHz, is used as a prototype for MIMO system testing and performance analysis. Genetic Algorithm (GA) is used to optimize, to determine the antenna physical parameters and to improve its performance. The mutual impedance concept is used to model the MC. The particle swarm optimization (PSO) algorithm is used to optimize the channel throughput and to reduce the distance between the elements. Simulation results show that it is possible to obtain a MIMO system performance improvement of 11.1% in channel throughput, the microstrip width is reduced by 14.5% and the distance among the elements can be reduced by 23.7%, considering the MC.摘要 本文提出一种基于仿生优化算法的模型，用于调控多输入多输出（Multiple-Input Multiple-Output, MIMO）系统中的互耦（Mutual Coupling, MC）效应。该方法通过搜索小于半波长的阵列单元间距参数，可提升信道吞吐量并缩减阵列单元间的距离。本文以26 GHz频段的微带天线设计作为原型，开展MIMO系统的测试与性能分析工作。采用遗传算法（Genetic Algorithm, GA）优化并确定天线的物理参数以提升其性能，借助互阻抗概念对互耦效应进行建模，并采用粒子群优化（Particle Swarm Optimization, PSO）算法优化信道吞吐量、进一步缩小阵列单元间的间距。仿真结果表明，在考虑互耦效应的前提下，MIMO系统的信道吞吐量可提升11.1%，微带贴片宽度缩减14.5%，阵列单元间距可降低23.7%。