认知无线网络频谱感知与频谱分配算法研究

发布时间：2018-06-18 14:03

本文选题：认知无线电 + 频谱感知　；参考：《北京邮电大学》2015年硕士论文

【摘要】：随着通信技术的发展,人们对无线电频谱资源的需求日益强烈,为提高频谱利用率,认知无线电技术应运而生。认知无线电技术是指认知用户可以动态感知空闲频谱,并在保证不对授权用户造成干扰的前提下择机接入频谱进行通信的技术。频谱感知与频谱分配是认知无线技术的两个重要研究内容。本课题以频谱感知与分配的智能优化算法研究为核心,分别对认知网络的协同频谱感知和蜂窝异构网络的频谱分配问题进行了研究,提出了相应的智能优化算法。对于认知网络的协同频谱感知技术的研究,论文首先分析了认知网络模型,并以使系统获得较大的检测概率为目标,提出了适用于频谱感知问题的连续量子蛙跳算法。通过该算法为感知可靠度不同的认知用户赋予不同的权重,进行协同感知。该算法具备比传统智能算法更高的收敛精度和更快的收敛速度,可在相同的虚警概率下,使认知网络获得更大的检测概率。对于认知异构网络频谱分配技术,研究分为两部分：首先进行单目标优化问题的研究,将量子的概念引入粒子群算法,针对粒子演进过程中易陷入局部最优解的问题,提出了两种离散量子粒子群优化算法——多变量量子粒子群优化算法和混合量子粒子群优化算法,并将其应用于认知无线电频谱分配这一离散优化问题,其中,算法的每一个即为一种频谱分配方案；在此基础上,提出了多目标量子粒子群优化算法,解决了具有多个网络效益目标函数的频谱分配问题。本文提出的单目标量子粒子群优化算法具备比现有智能算法更高的收敛精度,可使网络获得更大的网络效益；本文提出的多目标量子粒子群优化算法实现了最大网络效益和最大比例公平网络效益的兼顾。
[Abstract]:With the development of communication technology, the demand for radio spectrum resources is increasingly strong. In order to improve spectrum efficiency, cognitive radio technology emerges as the times require. Cognitive radio technology refers to the technology that cognitive users can dynamically perceive the idle spectrum and access the spectrum without interfering with the authorized users. Spectrum sensing and spectrum allocation are two important contents of cognitive wireless technology. Based on the research of the intelligent optimization algorithm of spectrum sensing and allocation, this paper studies the cooperative spectrum sensing of cognitive networks and the spectrum allocation of cellular heterogeneous networks, and puts forward the corresponding intelligent optimization algorithms. For the research of cooperative spectrum sensing in cognitive networks, this paper first analyzes the cognitive network model, and proposes a continuous quantum leapfrog algorithm for spectrum sensing. The algorithm assigns different weights to cognitive users with different perceptual reliability and carries out cooperative perception. The proposed algorithm has higher convergence accuracy and faster convergence speed than the traditional intelligent algorithm, which can make the cognitive network obtain a higher detection probability under the same false alarm probability. The spectrum allocation technology of cognitive heterogeneous networks is divided into two parts: firstly, the single objective optimization problem is studied, and the concept of quantum is introduced into particle swarm optimization algorithm to solve the problem that particle evolution is prone to fall into local optimal solution. Two discrete quantum particle swarm optimization algorithms, multivariable quantum particle swarm optimization and hybrid quantum particle swarm optimization, are proposed and applied to the discrete optimization problem of spectrum allocation of cognitive radio. Based on this, a multi-objective quantum particle swarm optimization (QPSO) algorithm is proposed to solve the spectrum allocation problem with multiple network benefit objective functions. The single objective Quantum Particle Swarm Optimization (QPSO) algorithm proposed in this paper has higher convergence accuracy than the existing intelligent algorithm and can make the network gain more network benefits. The multi-objective quantum particle swarm optimization (QPSO) algorithm proposed in this paper realizes both the maximum network benefit and the maximum proportional fair network benefit.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN925

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