【作者】 郑桢；

【导师】 田宝玉；

【作者基本信息】 北京邮电大学 ， 信号与信息处理， 2013， 硕士

【摘要】 随着无线通信技术的发展和人们对无线通信传输速率、质量等要求的提高,OFDM技术已经成为第四代移动通信的关键技术。准确、实时的信道估计和PAPR抑制算法是OFDM系统的两个主要问题。MIMO-OFDM技术将时间分集、空间分集与频率分集三者有机结合,有效抵频率选择性衰落,使频谱利用率有很大提高。MIMO-OFDM系统需要各种分集的利用,相干解调解码等都需要用到信道估计信息。PAPR过高是OFDM系统的另一个问题,造成系统的性能降低,对PAPR抑制算法可以有效降低PAPR带来的损失。目前,压缩感知(Compressed Sensing,CS)是信号处理领域一门新兴的技术,能够少量的压缩信号中恢复出原始信号。论文把CS引入到OFDM系统的信号处理中,研究基于CS的信道估计和PAPR抑制算法,重点介绍如何把CS用于MIMO-OFDM信道估计。主要内容安排如下。第一章介绍了论文的研究OFDM系统,MIMO-OFDM及压缩感知技术的背景,关键技术和文章安排顺序。第二章介绍CS的关键理论,给出了几种实用的重构算法,Orthogonal Matching Pursuit (OMP)算法,Basis Pursuit(BP)算法,SOMP算法等算法的原理与实现步骤。第三章重点介绍了BCS算法和稀疏信号的树结构,提出了改进的算法TBCS,较BCS算法取得了更好地算法性能。第四章介绍无线衰落信道特性,MIMO, MIMO-OFDM目关技术,MIMO-OFDM系统框架,已及常用的OFDM系统中的信道估计方法。第五章首先介绍了MIMO-OFDM的信道模型,考虑MIMO信道本身具有稀疏性,信道之间具有相关性,构建一组相关的稀疏信道,并根据FFT变换的特点,利用DCS对MIMO子信道进行联合信道估计,仿真验证了基于CS的信道估计方案比原来的LS信道估计方案有更好的估计效果。第六章研究了目前CS在PAPR抑制算法上的应用。对当前OFDM系统的PAPR抑制算法进行分类研究。在此基础上,研究了应用CS技术的PAPR抑制算法,验证了CS对于降低PAPR有很好的应用前景。第七章对本文的工作进行总结,给出了今后的研究方向。更多还原

【Abstract】 As the development of wireless communication technologies and the improvement of need for transfer rate and quality in wireless communication. OFDM has been a key technology in4th wireless communication. Accurate channel estimation and Peak to Average Power Ratio (PAPR) are two major problems in OFDM. MIMO-OFDM can obtain time diversity, spaitial diversity and frequency diversity at the same time, so it can combat frequency selective fading and improve spectrum efficiency greatly. To achieve good performance, channel state information is necessary in MIMO-OFDM. High PAPR is another problem in OFDM, which make the system performance, so we using PAPR alrithmns to reduce the loss of PAPR.Compressive Sensing (CS) is a new technology in signal process whitch can recover signal from some measurements.this paper introduces CS into OFDM system to process signal, and do rearch on channel eatimation and PAPR. This paper studies how to use CS to MIMO-OFDM channel estimation, and the main contents are described as followings.Chapter1introduces the background of OFDM system, MIMO-OFDM and CS technology, key technologies and organization of the paper.Chapter2intruduce CS key theory, put forward some practical reconstruction algorithms, Orthogonal Matching Pursuit (OMP),Basis Pursuit(BP), SOMP and their algorithm phaseso on.Chapter3introduces BCS algorithm and sparse tree structure of signal. Put forward an improved algorithm TBCS, which achieves achieved a better performance compared to the BCS algorithm.Chapter4describes the features of channels and introduces wireless communication fading channels’background. Introduces technologies of MIMO, MIMO-OFDM, MIMO-OFDM systems framework, and common channel estimation methods in OFDM system Chapter5first introduces MIMO-OFDM channel model, considering MIMO channel itself sparsity correlation, build a set of related sparse channel. Use DCS to estimate MIMO channels.and simulations verify that CS channel estimation scheme has advantage over original LS estimated scheme.Chapter6studies current PAPR algorithm based on CS. In this chapter, we research on existing work of using CS to reduce PAPR, and bring good performance. Based on research, the application of CS to PAPR suppression algorithm verifies good prospect of application of CS to reduce PAPR.Chapter7summaries this paper’s work, give future research directions.更多还原