快时变MIMO OFDM信道建模分析与信道估计技术研究

发布时间：2018-06-21 10:07

本文选题：MIMO + OFDM　；参考：《西南交通大学》2014年硕士论文

【摘要】：未来无线通信系统需要满足在高速移动的环境下能够提供高速、可靠的数据传输的要求,对于MIMO OFDM系统,数据率的提高需要更大的带宽,而带宽的增大会使信号的采样间隔小于信道的时延扩展,在多径传播环境中信道出现频率选择性衰落,引起符号间干扰(ISI, InterSymbol Interference)。同时移动台和基站之间的相对移动带来多普勒扩展,使传输信道随时间快速变化,造成信道的时间选择性衰落,引起子载波间干扰(ICI,InterCarrier Interference)。在这种双选择性衰落信道的条件下,接收端获取准确的信道信息对后续信道均衡等处理具有重要的意义。论文在分析MIMO OFDM系统时频双选择性衰落信道特征的基础上,利用基扩展模型(BEM, Basis Expansion Model)刻画时变信道以减少信道参数个数,并且分析比较不同的基扩展模型的拟合效果。基于这种信道建模方法,利用卡尔曼滤波器和最大似然算法分别进行信道估计。其次,论文从信道本身的稀疏性出发,研究快衰落信道在时延多普勒域的稀疏表示,推导出基于稀疏表示下的接收信号模型,结合压缩感知理论,利用传统正交匹配追踪(OMP, Othogonal Matching Pursuit)算法重构出信道信息。由于传统OMP算法中设定的信道稀疏度值对信道估计性能存在影响,论文提出了一种不依赖于信道稀疏度预设值的改进的OMP算法,确保算法的稳定良好性。由于实际系统中存在载波频偏的影响,因此论文考虑了接收端存在载波频率偏移下的快衰落信道和频偏联合估计技术。基于基扩展模型的MIMO OFDM系统中,利用扩展卡尔曼滤波器对信道和频偏进行跟踪估计,除了这种方法外,也可采用期望最大算法(EM, Expectation Maximization)结合卡尔曼滤波器迭代的估计出信道和频偏。论文提出了一种低复杂度的改进EM算法,通过最大似然算法估计的频偏作为EM算法中频偏的粗估计值,并且根据迹函数性质改进Q函数,达到降低算法复杂度的目的。论文仿真分析表明,与原EM算法的性能相比,基于改进EM算法的复杂度的大幅度降低对其算法性能并未造成损失。
[Abstract]:The future wireless communication system needs to meet the requirements of high-speed and reliable data transmission in high-speed mobile environment. For MIMO OFDM system, the increase of data rate needs more bandwidth. With the increase of bandwidth, the sampling interval of the signal is smaller than the delay spread of the channel, and the frequency selective fading occurs in the multipath propagation environment, which leads to ISI and Intersymbol interference. At the same time, the relative movement between the mobile station and the base station leads to Doppler expansion, which makes the transmission channel change rapidly with time, resulting in the time-selective fading of the channel, and induces the intercarrier interference (ICI) Intercarrier interference (ICI). Under the condition of double selective fading channel, it is important for the receiver to obtain accurate channel information for subsequent channel equalization processing. On the basis of analyzing the characteristics of time-frequency and dual-selective fading channels in MIMO OFDM systems, the time-varying channels are characterized by the basis expansion model (BEM, basis expansion Model) to reduce the number of channel parameters, and the fitting results of different base expansion models are analyzed and compared. Based on this channel modeling method, Kalman filter and maximum likelihood algorithm are used for channel estimation. Secondly, based on the sparsity of channel itself, the sparse representation of fast fading channel in time-delay Doppler domain is studied, and the received signal model based on sparse representation is derived, combined with compression sensing theory. The channel information is reconstructed by using the traditional orthogonal matching tracking (OMP) and Othogonal matching pursuit (OMP) algorithm. As the channel sparsity values set in the traditional OMP algorithm affect the channel estimation performance, an improved OMP algorithm is proposed, which does not depend on the channel sparsity presupposition, to ensure the stability of the algorithm. Because of the influence of carrier frequency offset in the actual system, the fast fading channel and the joint frequency offset estimation technique are considered in this paper. In MIMO OFDM systems based on the base spread model, the extended Kalman filter is used to track and estimate the channel and frequency offset. The channel and frequency offset can also be estimated by using the expected maximum algorithm (EMM, expectation maximization) and Kalman filter iteration. In this paper, an improved EM algorithm with low complexity is proposed. The frequency offset estimated by the maximum likelihood algorithm is regarded as the rough estimation value of the intermediate frequency offset of the EM algorithm, and the Q function is improved according to the property of the trace function to reduce the complexity of the algorithm. The simulation results show that compared with the performance of the original EM algorithm, the complexity reduction based on the improved EM algorithm does not cause any loss to the performance of the improved EM algorithm.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN919.3

【共引文献】