Single-Objective Genetic Algorithm for dynamic optimization of reconfigurable antenna systems

Modern satellite telecommunications are moving toward the use of large capacity systems exploiting fade mitigation techniques such as reconfigurable on-board antenna systems to face channel attenuation. In this work we propose a new Single-Objective Genetic Algorithm (SOGA) able to optimize the spatial distribution of the radiated power, taking into account the effect of non-ideal antenna components. A preliminary analysis shows that the genetic algorithm, due to its flexibility, is more effective in handling the system non-ideality than standard optimization methods based on a deterministic approach.

I. Introduction

Satellite communication systems are moving toward the use of large system capacity and, consequently, higher frequencies (Ka, Q-V bands) to accomplish the user's requirement in terms of broadband services. To counteract the large tropospheric attenuation values occurring in the microwave frequency range, telecommunication systems have recently pushed the research effort toward the study and implementation of Propagation Impairments Mitigation Techniques (PIMT). One of such methods is based on reconfiguring the antenna radiation pattern according to the dynamics of weather throughout the satellite service area. The principles of a possible reconfigurable antenna system have been already discussed [1] and the performance achievable by simple optimization methods have been shown as well [2]. This work aims at investigating the application of Single-Objective Genetic Algorithms (SOGA) to improve the adaptive performance of a reconfigurable antenna system. The objective is to compute the best distribution of the power flux across the coverage area so as to minimize the number of non-served users, while, at the same time, ensuring an adequate amount of received power at the ground terminals to compensate the effects of a non-ideal transmission system.