Genetic algorithm applied to beamspace‐multiple‐input and multiple‐output single‐radio frequency front‐end reconfigurable transceivers

Abstract

The multiple‐input and multiple‐output (MIMO) capabilities of electronically steerable passive array radiator (ESPAR) antennas in the beamspace (BS) domain have designated them a strong candidate for BS‐MIMO implementations using transceivers with a single‐radio frequency front‐end. The core functionality of the ESPAR is based on the multiplexing of information symbols in the BS domain, using orthogonal basis patterns. The radiated pattern of the antenna is produced as the linear combination of the basis patterns weighted by the information symbols to be transmitted. The adequate approximation of the desired radiation pattern produced by the antenna is of high importance in order to retain the orthogonality between the basis patterns and the corresponding information streams. The beamforming in ESPAR antennas is achieved by applying the appropriate set of loads at the parasitic elements. Nevertheless, because of the non‐linear relationship between the radiation pattern and the loads of antenna elements, the calculation of these loads is not a trivial task. This paper presents a novel genetic algorithm (GA), designed to calculate the loading values of the parasitic elements of an ESPAR used by BS‐MIMO systems in realistic channel environments. The performance evaluation of the proposed GA reveals a robust and valuable optimisation tool even for demanding applications.

GA-MIMO: Genetic Algorithm for Optimization of ESPAR Antenna on Beamspace MIMO

“…To achieve the best solution, GA should have a function to compare the two radiation patterns in the term of correlation. The function called as fitness function [4] and it can be seen in Equation ( 6). Fitness max ( ) ( )…”

“…Physically, the radiation pattern of ESPAR antenna is obtained by determine reactance value on each parasitic element. However, the calculation of the ESPAR antenna reactance becomes quite complex because antenna geometry has a nonlinear relationship with the antenna radiation pattern [4]. On the other hand, Genetic Algorithm (GA) is a technique of optimization for obtaining the best solution from a set of solutions that have a certain opportunity with genetic operators to get the optimum solution from the many possible solutions [5] and [6].…”

“…On the other hand, Genetic Algorithm (GA) is a technique of optimization for obtaining the best solution from a set of solutions that have a certain opportunity with genetic operators to get the optimum solution from the many possible solutions [5] and [6]. Further, GA have been used much on electromagnetic fields such as optimization on combination of configuration switches [7] and optimization on combination of reactance on five elements ESPAR antenna [4]. At [4], the correlation of radiation pattern between desired and achieved pattern in channel ignorant conditions has a median value of 99.6%, 99.6% and 99.5% on QPSK, 8-PSK, and 16-QAM modulation, respectively.…”

“…Further, GA have been used much on electromagnetic fields such as optimization on combination of configuration switches [7] and optimization on combination of reactance on five elements ESPAR antenna [4]. At [4], the correlation of radiation pattern between desired and achieved pattern in channel ignorant conditions has a median value of 99.6%, 99.6% and 99.5% on QPSK, 8-PSK, and 16-QAM modulation, respectively.…”