基于改进压缩感知匹配追踪算法的认知无线电信道估计

发布时间：2018-03-31 22:01

本文选题：信道估计　切入点：认知无线电　出处：《燕山大学》2014年硕士论文

【摘要】：随着无线通信技术的发展，人们对无线频谱资源的需求日益增长。认知无线电技术（Cognitive Radio，CR）作为一种能够有效地解决有限频谱资源与日益增长的频谱需求之间矛盾的技术手段，得到了国内外学者的广泛关注。其中，无线信道的信道估计方法作为认知无线电系统中的关键技术，其性能的优劣直接关系到认知无线电通信质量的好坏，因此具有重要的研究意义。近年来，压缩感知技术成为信号处理和无线通信领域里的研究热点。由于无线多径信道的时域模型可以等效为一个横向滤波器，且其抽头稀疏分布具有稀疏性，表明运用压缩感知技术进行信道估计具备可行性。本文以降低系统开销以及提高信道估计性能为目标，研究基于压缩感知算法的认知无线电信道估计方法。首先，，在对认知无线电和压缩感知技术的研究背景进行介绍的基础上，对现有传统信道估计方法进行了分析总结，并论述了运用压缩感知技术进行认知无线电信道估计的可行性。其次，对简化粒子群算法进行改进，提高了粒子群算法的全局寻优特性，并采用改进后的简化粒子群算法对压缩感知弱匹配追踪算法进行优化，从而能够快速准确地搜索匹配稀疏信号的最优原子。在信号重构阶段引入一种改进阈值降噪策略，克服了传统的硬阈值降噪和软阈值降噪方法的不足。对某稀疏信号进行信号处理的仿真结果验证了该改进压缩感知算法的有效性。最后，针对非连续正交频分复用系统下的无线传输信道估计问题，基于传统的正交匹配追踪算法，通过引入快速选择及优胜劣汰机制，提高了搜索最优原子的快速性，保证了所选原子的最优性。针对宽带干扰和窄带干扰两种场景进行的信道估计，仿真结果表明与传统的最小二乘和正交匹配追踪算法相比，本文算法所重构出的信道与原始信道之间的均方误差MSE更小，传输信号误比特率BER更低，导频数目更少，是一种更加有效地进行无线传输信道参数估计的信道估计方法。
[Abstract]:With the development of wireless communication technology, the demand for wireless spectrum resources is increasing. Cognitive Radio (CR) is a kind of technology which can effectively solve the contradiction between the limited spectrum resources and the growing spectrum demand. The channel estimation method of wireless channel is a key technology in cognitive radio system, and its performance is directly related to the quality of cognitive radio communication. Therefore has the important research significance. In recent years, compression sensing technology has become a research hotspot in the field of signal processing and wireless communication, because the time-domain model of wireless multipath channel can be equivalent to a transverse filter, and its tap sparse distribution is sparse. This paper aims at reducing the system overhead and improving the performance of channel estimation, and studies the cognitive radio channel estimation method based on compressed sensing algorithm. Firstly, on the basis of introducing the research background of cognitive radio and compressed sensing technology, the existing traditional channel estimation methods are analyzed and summarized. The feasibility of cognitive radio channel estimation using compressed sensing technology is also discussed. Secondly, the simplified particle swarm optimization algorithm is improved to improve the global optimization characteristics of the particle swarm optimization algorithm, and the improved simplified particle swarm optimization algorithm is used to optimize the compression perception weak matching tracking algorithm. Thus, the optimal atom matching sparse signal can be searched quickly and accurately, and an improved threshold de-noising strategy is introduced in the signal reconstruction phase. The shortcomings of the traditional hard threshold denoising and soft threshold denoising methods are overcome. The simulation results of signal processing for a sparse signal verify the effectiveness of the improved compression sensing algorithm. Finally, aiming at the channel estimation problem of wireless transmission in discontinuous orthogonal frequency division multiplexing system, based on the traditional orthogonal matching tracking algorithm, by introducing the mechanism of quick selection and survival of the fittest, the rapidity of searching optimal atoms is improved. The channel estimation of wideband interference and narrowband interference is carried out, and the simulation results show that compared with the traditional least squares and orthogonal matching tracking algorithms, The proposed algorithm has smaller mean square error (MSE), lower bit error rate (BER) and fewer pilot frequencies between the channel and the original channel. It is a more effective channel estimation method for wireless transmission channel parameters estimation.
【学位授予单位】：燕山大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN925

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