Optimization Algorithm And Applications Research For Reconfigurable Antenna System

The evolving wireless communication technology has a higher requirement for antenna. In this case, the reconfigurable antenna has got a rapid development for the characteristic of wide frequency band and self-adaptive reconfiguring. This paper mainly studies the optimization algorithm of reconfigurable antenna system and the way to accelerate the convergence of it. The applied research of reconfigurable antenna in the other communication system has also presented in this paper. The main contents are as follows:1. Firstly, the structure and principle of reconfigurable antenna and its system are introduced as well as its classification. In this part, the introduction of three commonly used optimization algorithms and their application in reconfigurable antenna is also given. And these contents are the foundation of this paper.2. A Guided Self-adaptive Evolutionary Genetic Algorithm(GSEGA) is proposed. In this algorithm, the principle of good point set is used to generate the initial population. Based on the elitist preserved method, a way of parallel crossing and mutation with population-segmentation is offered, in which a son population among the segmented population is randomly generated. In addition, a guided self-adaptive mutation strategy based on the statistics of the more excellent individualities is adopted on the other part of the son population to speed up the evolution. Through the use of the homogeneous finite Markov chain model, the global convergence and high searching speed of the GSEGA is proved. The experimental results show that the GSEGA presents a higher speed and precision in comparison with the other mentioned Genetic Algorithms(GAs) as mentioned.3. A practical reconfigurable antenna, usually equipped w ith multiple switches, has a high real-time requirement for the optimization algorithm. In this paper, we therefore propose a self-adaptive induced mutation algorithm(SIMA) which is based on GA and has a fast convergence. SIMA first determines the “key switches” by analyzing the distribution of the switch states of lower standing- wave ratio in the evolution. The configurations of worse states are then induced to set up their “key switches” to “excellent statues.” Experiments on the optimization of a 39-switch reconfigurable antenna system at 50 MHz, 200 MHz and 350 MHz demonstrate that the convergence rate of SIMA is at least 2.15 times that of the genetic algorithm. A practical reconfigurable antenna system, constituted with an antenna template, a signa l receiver, a signal source, and a PC, is built. The fitness function of the optimization algorithm is the power spectral value at the operating frequency. Tests at three different frequencies are operated on the system and their power spectral before and after the optimization are given in this paper. The results show that the proposed algorithm can significantly improve the performance of the antenna system.4. The applications of reconfigurable antenna in cognitive radio technology are presented. This part focuses on the performance comparison in terms of SNR improvement between s of fixed antennas selection and reconfigurable antennas selection. The formula of probability density function(PDF) and cumulative distribution function(C DF) is derived for link power and its mean of multiple- input multiple-output(MIMO) system. Theoretical analysis and simulation for reconfigurable receiving and transmitting antenna selection with respectively up to 4 antennas and 2 reconfigurable statuses are also presented. Results show that the performance improvement due to reconfigurable antenna for MIMO system is 76.5% of that of fixed antennas. An optimization algorithm, in which the antenna gain on the objective direction is chosen to be the fitness function, is designed for MIMO system. Simulation results show that the algorithm has both the ability of maximizing the antenna gain on the target direction and restraining the interference.