Utilização de inteligência artificial no controle do acoplamento mútuo em projetos de sistemas MIMO.

Abstract

This thesis presents a methodology that uses bio-inspired optimization algorithms to increase the channel capacity in Multiple Input Multiple Output (MIMO) systems considering mutual coupling (MC) to promote a reduction in the distance among the array elements. The method starts with the microstrip antenna design, at the frequency of 26 GHz, that is used as a prototype for MIMO system testing and peformance analyses. The antenna is simulated using the CST Studio Suite® software, and Genetic Algorithms (GA) are used to optimize, to determine the antenna’s physical parameters and to improve its performance. The method considers the MC among antenna array elements in the signal transmission and reception. The Conventional Mutual Impedance Method (CMIM) and the Receiving Mutual Impedance Method (RMIM) were chosen to be used in the simulation, because of their adherence to real situations. The Particle Swarm Optimization algorithm (PSO) is used to optimize the channel capacity and to reduce the distance among the elements with a specific modification for the problem that uses particle in acceleration strategy in a specific part of the search space. This thesis presents a method of analyzing the results in reception mode as a function of channel capacity. Simulation results show that it is possible to obtain a MIMO system performance improvement of 11.1% in channel capacity, and the distance between elements can be reduced by 23.7%, considering the MC.