【作者】 李鑫；

【导师】 李世平；

【作者基本信息】 东北大学 ， 信号与信息处理， 2012， 硕士

【摘要】 高速率和高质量的通信服务,是未来无线移动通信系统的目标和需求。正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)技术是一种高速多载波传输技术,具有较高的频谱利用率和较强的抗多径衰落能力,是下一代移动通信系统中实现高速数据传输的核心技术。无线移动信道是现代通信系统中最为复杂的信道,由于OFDM系统中信道的频率选择性和时变特性,为了在接收端实时而准确的进行相干解调,需要知道信道的状态信息,就必须对信道进行估计。信道估计作为OFDM系统的一个重要组成部分,其研究主要可以归纳为两种：一种是盲信道估计,另一种是基于导频的信道估计。由于盲信道估计算法的复杂度高,收敛速度慢,灵活性差,不适于在实际中应用,因此,本文主要针对基于导频的信道估计进行研究。文中首先分析了OFDM系统的基本原理和模型以及无线信道的传播特性。然后研究了几种信道估计算法,并通过仿真分析比较各种算法的性能。重点研究了基于离散傅里叶变换(Discrete Fourier Transform, DFT)的信道估计算法,针对传统算法中的不足,提出了两点改进：(1)在整数倍的采样间隔信道下,传统算法只是去除了循环前缀长度以外的噪声,未对循环前缀长度以内的噪声做任何处理,从而影响了算法的性能。针对这个问题,提出了一种改进算法,通过在循环前缀长度内引入判决门限值,将信道能量点与噪声区分开,进一步消除噪声的影响,提高系统估计的性能,通过仿真验证了改进算法的有效性。(2)在非整数倍的采样间隔信道下,传统算法会导致信道冲激响应能量的泄露,降低信道的估计性能。针对这个问题,提出了通过计算能量增长率来选择保留有效路径的方法,进而提高信道估计性能,通过仿真验证了改进算法的有效性。研究了二维快速傅里叶变换(Fast Fourier Transform, FFT)插值信道估计算法,但插值算法容易带来噪声误差,导致估计结果不够准确。针对这一问题,提出了一种改进方法,通过将LMS滤波算法应用到信道估计中,在二维FFT插值后进行LMS自适应信道估计,LMS算法能够根据输入信号的统计特性变化来不断调整滤波器的权系数,进一步减小噪声误差,以获得最优的信道估计值,仿真结果验证了改进算法的有效性。更多还原

【Abstract】 Communication service with high speed and high quality is required in future wireless mobile communication system. OFDM technique is a kind of high-speed multi-carrier transmission technique, which has high utilization of frequency spectrum and excellent capability of countering multi-path interference. This makes it key technique in the next generation mobile communication system. The wireless mobile channel is the most complex channel in the modern communication system. Since the channel of OFDM system has frequency selective and time-varying characteristics. It is necessary to estimate channel so that the channel state information can be obtained at receiving end and coherent demodulation is realized accurately and in time. The research of channel estimation in OFDM system can be conducted in two categories:one is based on blind channel estimation, the other is channel estimation based on pilots. Because of the high algorithm complexity, the slow convergence and the bad flexibility of the blind channel estimation, it is not suitable in practical use. So this paper focuses on the research of channel estimation based on pilots.In this paper, the basic principle and model of OFDM system and the characteristics of wireless channel are analysed firstly. Then several channel estimation algorithms are researched. And the performance comparisons of these algorithms are made by computer simulation. This paper mainly studies DFT-based channel estimation in OFDM system, and provides two improvements for the shortage of the conventional algorithm:(1) When the channel is sample-spaced multipath channel, the conventional algorithm only removes the noise in the channel impulse response beyond the length of the cyclic prefixs (CP), while the noise within the length of CP is not suppressed at all, affecting the performance of the algorithm. An improved algorithm is provided for this problem. Only by introducing a decision threshold into the length of CP, the influence of the noise could be further removed by threshold processing, and the performance of channel estimation is improved. At last, the simulation results prove the effectiveness of the modified algorithm.(2) When the channel is non-sample-spaced multipath channel, the conventional method will lead to energy leakage of the channel impulse response, and reduce the channel estimation performance. An improved algorithm is present to solve this problem. By calculating the changing rate of the power to select the effective path, and further improve the performance of channel estimation. At last, the simulation results prove the effectiveness of the modified algorithm.The two-dimensional FFT interpolation channel estimation algorithm is researched. However, the channel estimation algorithm is easy to bring noise error, leading to the problem of insufficient precision. To solve this problem, LMS filtering algorithm is applied to channel estimation. According to the changing statistics of the inputsignal, LMS algorithm constantly adjusts the filter weights and further reduces noise in order to achieve optimal output. At last, the simulation results prove the effectiveness of the modified algorithm.更多还原