OFDM系统的信道估计技术研究
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摘要
正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术是无线宽带数字通信系统中最近几年受到广泛关注的一种通信技术应用。它能够很好的抵抗由于无线信道多径时延扩展产生的符号间干扰。相干OFDM系统的接收端使用相干检测技术,系统需要知道信道状态信息(Channel State Information, CSI)以对接收信号进行信道均衡,从而信道估计成为系统接收端一个重要的环节。
本文首先对研究的基本理论进行了阐述,介绍了信道估计的实际应用。然后主要对无线OFDM系统中的信道估计做了如下研究：
首先分析了互相关(Cross Relation, CR)算法的基本原理,研究了多通道LMS盲辨识算法和牛顿算法。对LMS算法的通用最优步长的设计进行了详细的探讨,最后得到了基于互相关迭代算法的SIMO-OFDM系统盲信道估计仿真结果。
然后研究了典型的ZP-OFDM系统中子空间算法的辨识条件,分析了利用标准正交迭代方法估计噪声子空间进行信道估计的可行性。为了达到良好的算法估计性能,提高收敛速度,提出了两种改进的标准正交迭代算法跟踪子空间---LMS-NEWTON标准正交和改进的LMS-NEWTON标准正交。为了进一步达到快速跟踪子空间、降低算法复杂度的目的,采用降秩正交迭代的方法进行跟踪,另外自相关矩阵的逆和自相关矩阵具有相同的特征向量,因此提出一种快速的噪声子空间降秩迭代方法。
对于MIMO-OFDM系统信道估计方法也进行了研究和讨论。为了在发射天线的个数大于接收天线时也能够进行信道估计,对MIMO信道进行了过采样。过采样的噪声不再是白色的,采用预白化的方法来解决。并且提出利用导频或训练序列的半盲算法实现预白化问题。
最后利用子空间拟合技术,扩展了OFDM系统的信道估计技术。将传统子空间拟合估计和迭代拟合方式相结合,避免了传统子空间方法中的奇异值分解,得到一种OFDM迭代信道估计算法。为了避免子空间分解而产生的巨大的计算工作量,同时具有较高的收敛速度,能够跟踪时变数据的子空间,提出了基于共轭梯度算法的子空间拟合的OFDM信道估计算法。由于共轭梯度算法和多级维纳滤波器本质上的一致性,提出了基于Krylov子空间的OFDM半盲信道估计算法。
The application of Orthogonal Frequency Division Multiplexing(OFDM) in wireless broadband digital communication has drawn more and more attention in recent years, because it has been proved to being good at combating the Inter-Symbol Interference(ISI)caused by time dispersion of radio channel. In OFDM system, the coherent demodulation requires the Channel Status Information (CSI) being known by the receiver through channel estimation, which makes the channel estimation a key technology in the OFDM system. First, the dissertation introduces the fundamentals of researches, and the application of channel estimation. Then the dissertation discusses the channel estimation of wireless OFDM system as follows:
Firstly, the principles of cross-relation are introduced, and blind multichannel LMS method and Newton method are emphasized. The design of generalized optimal stepsize of LMS method is discussed in detailed, and a blind channel estimation simulation of SIMO-OFDM system is obtained based on CR iterativeness.
Secondly, the conditions of typical subspace method of ZP-OFDM system are studied, analyzing the feasibility of estimating noise subspace by standard orthogonal iterative method. In order to improve the convergent speed and to obtain better estimation performance, two improved methods are proposed, LMS-NEWTON orthogonal iterative method and modified LMS-NEWTON orthogonal iterative method. Reduced rank orthogonal iterative method is applied to track subspace in order to track subspace fast and reduce the compute complexity. Since the inverse of autorelation matrix has the same eigenvectors with, another noise subspace reduced rank orthogonal iterative method is proposed. The simulation results show that both methods can achieve stable convergence.
Then, among channel estimation methods of MIMO-OFDM systems, oversampling is carried out to estimate MIMO channel when the number of transmit antenna is greater than that of the received antenna. After oversampled, noise is not white any more, so the prewhiten method is applied. The semi-blind method with pilots or training sequences is proposed to solve the prewhiten problem. The simulation results show that reduced rank orthogonal iterative method could achieve to good the estimation of channel impulse response.
Last but not least, signal subspace fitting and noise subspace fitting are analyzed. Combined with weighted subspace fitting and iterative fitting method, a OFDM iterative channel estimation method is proposed. In order to avoiding the high computation and achieve high speed to track time-variated data subspace, a OFDM channel estimation based on subspace fitting with conjugate gradient method is proposed. Since the conjugate gradient method and multistage wiener filter are equivalent, an OFDM semi-blind channel estimation method based on Krylov subspace is proposed. The simulation results show that the three methods could accomplish the similar result to classical subspace fitting method, and improve the estimation performance, while the last two methods show better performance than the first one in convergent speed, tracking effect and computation complexity.
本文首先对研究的基本理论进行了阐述,介绍了信道估计的实际应用。然后主要对无线OFDM系统中的信道估计做了如下研究：
首先分析了互相关(Cross Relation, CR)算法的基本原理,研究了多通道LMS盲辨识算法和牛顿算法。对LMS算法的通用最优步长的设计进行了详细的探讨,最后得到了基于互相关迭代算法的SIMO-OFDM系统盲信道估计仿真结果。
然后研究了典型的ZP-OFDM系统中子空间算法的辨识条件,分析了利用标准正交迭代方法估计噪声子空间进行信道估计的可行性。为了达到良好的算法估计性能,提高收敛速度,提出了两种改进的标准正交迭代算法跟踪子空间---LMS-NEWTON标准正交和改进的LMS-NEWTON标准正交。为了进一步达到快速跟踪子空间、降低算法复杂度的目的,采用降秩正交迭代的方法进行跟踪,另外自相关矩阵的逆和自相关矩阵具有相同的特征向量,因此提出一种快速的噪声子空间降秩迭代方法。
对于MIMO-OFDM系统信道估计方法也进行了研究和讨论。为了在发射天线的个数大于接收天线时也能够进行信道估计,对MIMO信道进行了过采样。过采样的噪声不再是白色的,采用预白化的方法来解决。并且提出利用导频或训练序列的半盲算法实现预白化问题。
最后利用子空间拟合技术,扩展了OFDM系统的信道估计技术。将传统子空间拟合估计和迭代拟合方式相结合,避免了传统子空间方法中的奇异值分解,得到一种OFDM迭代信道估计算法。为了避免子空间分解而产生的巨大的计算工作量,同时具有较高的收敛速度,能够跟踪时变数据的子空间,提出了基于共轭梯度算法的子空间拟合的OFDM信道估计算法。由于共轭梯度算法和多级维纳滤波器本质上的一致性,提出了基于Krylov子空间的OFDM半盲信道估计算法。
The application of Orthogonal Frequency Division Multiplexing(OFDM) in wireless broadband digital communication has drawn more and more attention in recent years, because it has been proved to being good at combating the Inter-Symbol Interference(ISI)caused by time dispersion of radio channel. In OFDM system, the coherent demodulation requires the Channel Status Information (CSI) being known by the receiver through channel estimation, which makes the channel estimation a key technology in the OFDM system. First, the dissertation introduces the fundamentals of researches, and the application of channel estimation. Then the dissertation discusses the channel estimation of wireless OFDM system as follows:
Firstly, the principles of cross-relation are introduced, and blind multichannel LMS method and Newton method are emphasized. The design of generalized optimal stepsize of LMS method is discussed in detailed, and a blind channel estimation simulation of SIMO-OFDM system is obtained based on CR iterativeness.
Secondly, the conditions of typical subspace method of ZP-OFDM system are studied, analyzing the feasibility of estimating noise subspace by standard orthogonal iterative method. In order to improve the convergent speed and to obtain better estimation performance, two improved methods are proposed, LMS-NEWTON orthogonal iterative method and modified LMS-NEWTON orthogonal iterative method. Reduced rank orthogonal iterative method is applied to track subspace in order to track subspace fast and reduce the compute complexity. Since the inverse of autorelation matrix has the same eigenvectors with, another noise subspace reduced rank orthogonal iterative method is proposed. The simulation results show that both methods can achieve stable convergence.
Then, among channel estimation methods of MIMO-OFDM systems, oversampling is carried out to estimate MIMO channel when the number of transmit antenna is greater than that of the received antenna. After oversampled, noise is not white any more, so the prewhiten method is applied. The semi-blind method with pilots or training sequences is proposed to solve the prewhiten problem. The simulation results show that reduced rank orthogonal iterative method could achieve to good the estimation of channel impulse response.
Last but not least, signal subspace fitting and noise subspace fitting are analyzed. Combined with weighted subspace fitting and iterative fitting method, a OFDM iterative channel estimation method is proposed. In order to avoiding the high computation and achieve high speed to track time-variated data subspace, a OFDM channel estimation based on subspace fitting with conjugate gradient method is proposed. Since the conjugate gradient method and multistage wiener filter are equivalent, an OFDM semi-blind channel estimation method based on Krylov subspace is proposed. The simulation results show that the three methods could accomplish the similar result to classical subspace fitting method, and improve the estimation performance, while the last two methods show better performance than the first one in convergent speed, tracking effect and computation complexity.
引文
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