可重构天线系统寻优算法及应用研究

发布时间：2018-04-29 05:24

本文选题：可重构天线 + 寻优算法　；参考：《解放军信息工程大学》2014年硕士论文

【摘要】：随着无线通信技术的不断发展,对天线的要求越来越高,可重构天线以宽频带接收、自适应调整等特点在近几年得到了迅速发展。从提高实际可重构天线应用系统中的寻优速度角度出发,围绕可重构天线系统的寻优算法展开研究,并对可重构天线与其他通信系统的融合应用进行了一定的理论研究,主要内容如下:1.首先对可重构天线及其系统构成和原理展开了介绍,介绍了可重构天线的分类。对几种常用的仿生学全局优化算法以及在可重构天线系统寻优中的应用进行了简单的介绍,构成了本文的研究基础。2.在基本遗传算法基础上,提出一种自适应引导进化遗传算法。算法中采用佳点集方法产生初始种群,结合保留精英个体策略,对种群进行分割,各子种群并行交叉变异,且其中一个子种群为随机产生的。为提高算法收敛速度,分别对各子种群中较优个体进行优秀基因位统计,据此对其他个体采取一种自适应引导变异操作。通过将算法运行过程建模为有限齐次马氏链,证明了算法的全局收敛性和收敛快速性。实验结果表明,自适应引导进化遗传算法较其他提到的遗传算法在收敛速度和准确度上都有较大提高。3.针对实际可重构天线系统特别是多开关可重构天线系统在实际信号接收方面的实时性需求,在自适应引导进化遗传算法基础之上,提出了一种针对可重构天线系统的诱导变异算法。该算法通过对每一代可重构天线状态中的较优的开关组合分布进行分析,找出对天线性能影响大的“关键开关”并确定其最优状态下的开关的状态值-“优秀开关状态”。根据以上统计结果,每一代中较差的可重构天线状态在所有关键开关位的状态会被诱导变异成为相应的“优秀开关状态”。对39-开关的可重构天线进行仿真实验,结果显示,在50 MHz、200 MHz、350 MHz频率上的寻优过程中该算法收敛速度至少是基本遗传算法的2.15倍。利用同尺寸的实际天线系统对算法性能进行实际测试,该系统由可重构天线、接收机、信号源和PC构成,以信号在工作频点的功率谱值为适应度函数,在多个频点对可重构天线的状态寻优,给出了各频点的寻优前后功率谱。通过分析比较,可以发现文中提出的算法可使系统性能得到大幅提升。4.研究了可重构天线在认知无线电技术以及MIMO系统中的应用,重点分析了可重构天线阵与同等天线选择规模的固定天线阵相比在信噪比提升方面的性能对比。推导了多输入多输出系统链路功率的概率分布和平均值计算公式。对发射和接收端最大天线数和天线状态数分别为4和2的可重构天线阵进行理论分析和仿真实验,结果表明,通过天线状态选择,可重构天线阵在提高信噪比方面性能至少能达到同等规模的固定天线阵的76.5%。对MIMO系统的可重构天线状态寻优算法进行设计,以目标方向上天线增益为适应度函数,仿真结果显示本文算法不仅使天线在目标方向上增益最大,还具有抑制已知方向的干扰信号的潜能。
[Abstract]:With the continuous development of wireless communication technology, the requirements for the antenna are getting higher and higher. The characteristics of the reconfigurable antenna have been developed rapidly in the past few years. The fusion application of reconfigurable antenna and other communication systems has been studied in theory. The main contents are as follows: 1. first, the structure and principle of reconfigurable antenna and its system are introduced, and the classification of reconfigurable antennas is introduced. The application of some common bionics global optimization algorithms and the optimization of reconfigurable antenna systems is introduced. Based on the basic genetic algorithm, an adaptive guidance evolutionary genetic algorithm is proposed based on the basic genetic algorithm (.2.). In the algorithm, the best point set method is used to produce the initial population, and the elite individual strategy is used to divide the population, and the subpopulations are crossed and mutable in parallel, and one of the subpopulations is the following. In order to improve the convergence speed of the algorithm, an excellent gene bit statistics is carried out for the superior individuals of each subpopulation respectively. According to this, an adaptive guidance mutation operation is adopted for other individuals. By modeling the operation process of the algorithm to a finite homogeneous Markov chain, the global convergence and convergence speed of the algorithm are proved. The experimental results show that the algorithm is self-contained. The adaptive guidance evolutionary genetic algorithm (GA) has higher convergence speed and accuracy than the other genetic algorithms mentioned in this paper..3., based on the adaptive guidance evolutionary genetic algorithm (adaptive guidance evolutionary genetic algorithm), is based on the real time demand of the actual reconfigurable antenna system, especially the multi switch reconfigurable antenna system in the actual signal reception. The algorithm of induced mutation of the reconfigurable antenna system. By analyzing the better switch combination distribution in the state of each reconfigurable antenna, the algorithm finds the key switch which has a large impact on the performance of the antenna and determines the state value of the switch under the optimal state - "excellent switch state". The state of the poor reconfigurable antenna in all key switch bits will be induced to be induced to be the corresponding "excellent switch state". A simulation experiment on the reconfigurable antenna of the 39- switch shows that the convergence rate of the algorithm at 50 MHz, 200 MHz, and 350 MHz frequency is at least 2.15 times that of the basic genetic algorithm. The performance of the algorithm is tested with the actual antenna system of the same size. The system consists of a reconfigurable antenna, a receiver, a signal source and a PC. The power spectrum of a reconfigurable antenna is optimized at a number of frequency points. The power spectrum of each frequency point is given. In order to find the algorithm proposed in this paper, the performance of the system can be greatly improved by.4., and the application of reconfigurable antenna to cognitive radio and MIMO system is studied. The performance comparison of reconfigurable antenna array with the fixed antenna array with the same antenna selection size is analyzed. The multi input and multiple output is derived. The probability distribution and average value calculation formula of the system link power. The reconfigurable antenna array with the maximum number of antennas at the transmitter and receiver and the number of antenna states of 4 and 2, respectively, is analyzed and simulated. The results show that the performance of the reconfigurable antenna array can at least reach the same scale in improving the signal to noise ratio through the selection of the antenna state. The 76.5%. of the fixed antenna array is designed for the optimization algorithm of the reconfigurable antenna state of the MIMO system. The antenna gain is the fitness function in the direction of the target. The simulation results show that the algorithm not only makes the antenna gain the maximum in the direction of the target, but also has the potential of suppressing the known direction of the interference signal.

【学位授予单位】：解放军信息工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN820

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