基于PSAM的MIMO时间-频率双选择性信道的信道估计

数据的高速率传输以及终端的高速移动,导致无线通信信道具有时间选择性与频率选择性两个特征.本文主要研究了数据分组传输方式下,基于导频符号辅助调制(PSAM)的多输入多输出(MIMO)时间-频率双选择性信道的信道估计问题.首先,将时间-频率双选择性MIMO信道,建模为一个随时间变化的多项式内插信道模型;然后,根据信道Doppler衰落速率、多项式模型中的误差项,确定出模型的阶数以及整个数据块的长度;最后,基于该多项式内插信道模型,提出了采用PSAM的MIMO双选择性信道估计方法.实验结果表明本文的算法在时间-频率双选择性衰落信道下具有较好的性能.     

基于多项式模型的时频二维OFDM信道估计算法

本文提出了一种内插导频的时频二维信道估计算法,用于无线移动信道下的正交频分复用(OFDM)系统。该算法基于梳状内插的导频信号模型,采用多项式模型对信道进行拟合,从而能够精确的估计出信道信息。与时频二维维纳滤波以及自适应判决反馈的算法相比,该算法不需要预先知道信道相关矩阵以及信噪比等信道信息,简单易于实现,并且能够有效的抑制信道估计中的噪声干扰。仿真结果表明,在衰落环境下,该算法的信道估计最小方差(MSE)性能与最小二乘(LS)估计最小方差性能相比有显著提高。     

LTE下行信道估计算法研究

近年来,正交频分复用（OFDM）技术以其在高速数据传输中具有的良好的抗多径衰落的特性以及较高的频谱利用率等优点得到了广泛的应用。在OFDM通信系统中,信号在带宽小于信道相干带宽的多个正交子载波上发射和传输,以克服多径衰落信道所带来的信号频率选择性衰落。为了补偿各个子载波上的多径衰落,这就需要一个性能良好的信道估计方案。对LTE中基于导频的下行信道估计算法进行研究,首先在导频子载波处采用LS（最小二乘法）算法估计出信道参数,然后通过内插的方法在数据子载波处内插得到信道参数。频域内插主要采用拉格朗日内插或是LMMSE（线性最小均方误差）内插;时域内插采用拉格朗日内插。     

MC V-BLAST系统的MIMO信道盲估计

本文研究多载波垂直分层空时(MC V-BLAST)系统的下行频率选择性衰落多输入多输出(MIMO)无线信道估计问题.本文首先为MC V-BLAST系统提出了一种新颖的移不变性编码方法.利用上述移不变性性质,本文进一步提出了相应的下行频率选择性衰落MIMO无线信道的盲估计方法.仿真结果表明了本文移不变性编码方法的有效性和信道盲估计方法的性能.     

一种改进的MIMO-OFDM的信道估计算法

针对频率选择性衰落信道下MIMO—OFDM的系统,基于瑞利衰落信道的模型,利用m序列的自相关性,提出了一种基于m序列做梳状导频的多输入多输出正交频分复用最小二乘算法,该算法可以避免对大矩阵求伪逆,以减少计算复杂度,从而提高了信道估计的计算性能。通过对该算法的误码率性能分析和计算复杂度分析,结果表明,相比传统经典最小二乘算法,所提出信道估计算法在中低信噪比下,有效提高了信道估计性能,适合于在实际应用中实现。     

MIMO在对流层散射中的性能分析

首先,研究了MIMO空时分组编码(STBC)在平坦衰落信道下的应用以及相应的信道估计技术,并通过计算机仿真分析了STBC的性能以及信道估计对其性能的影响;接着,针对对流层散射信道特性,建立对流层散射信道的MIMO信道模型;最后,采用SC-FDE技术对抗对流层散射信道具有的频率选择性衰落,并仿真了该系统的性能,为STBC在对流层散射中应用奠定了基础。     

时间-频率双选择性衰落信道的最小二乘估计方法

高速移动下的无线宽带通信要经历时间和频率双选择性衰落,信道估计质量的好坏对整个系统的性能有重要的影响。该文利用复指数基扩展模型,提出一种基于最小二乘的时频双选信道估计方法,接收机不需要信道的统计信息,也不需要预先估计噪声方差,接收机复杂度较低;在该信道估计算法的基础上,基于信道估计均方误差最小准则,分析并推导出优化的训练序列所要满足的条件。仿真结果表明,在由Jakes模型产生的时频双选衰落信道下,在一定的信噪比范围内,该文方法的信道均方误差性能优于现有的方法。     

数字并行接收机在时变信道下的信道估计与均衡方法

为了适应高速宽带无线通信的需要,本文在一种高速数字并行接收机(APRX)结构的基础上,提出了一种时变信道下的信道估计和均衡方法。使用伪随机(pseudo-randomnumber,PN)序列相关进行信道估计,将所得到的信道频率响应粗估计按照一个DFT块的长度在一帧内进行线性内插得到信道频率响应细估计,将其用来在频域进行信道均衡。这种结构能适应高速率传输的要求,并且能有效地对抗时间和频率选择性衰落。仿真结果表明,在多径衰落信道下,APRX已经无法工作,而本文提出的数字并行接收机的信道估计和均衡方法有较好的性能,并且该方法实现简单,便于应用。     

MIMO-OFDM快衰落信道的稀疏自适应感知估计

在多输入多输出（MIMO）-正交频分复用（OFDM） 系统中,怎样在较高频谱利用率的情况下对快时变信道进行较为准确的估计是一个具有挑战性的课题。该文在利用压缩感知理论可提高系统频谱利用率的基础上,提出了一种适合于快时变环境下MIMO-OFDM 系统的稀疏自适应信道估计方法。该方法不再受到奈奎斯特采样频率条件约束,避免了传统导频辅助信道估计方法频谱利用率低的缺点。该文方法通过构建多天线群时频结构特征稀疏基,利用多天线间和群时变OFDM符号内信道冲激响应具有更强稀疏性的特点,对MIMO-OFDM快衰落信道进行稀疏变换。由于实际MIMO-OFDM快衰落信道往往处于频率选择性、时变性和多种干扰并存的复杂环境,受到干扰的信道参数对系统而言是未知,采用该方法克服了现有基于压缩感知理论的信道估计方法需要预先知道信道冲激响应稀疏度才能重构信道参数的不足,在信道稀疏度未知道的情况下,运用稀疏自适应的方法来对不同时频结构特征的信道参数进行估计。仿真结果表明所提估计方法具有对快时变信道参数估计的鲁棒性和较高频谱利用率,且均方误差小。      

基于Kronecker法的MIMO信道模拟及硬件实现

针对多输入多输出（Multiple-Input Multiple-Output,MIMO）无线通信系统,在基于Kronecker 的MIMO信道模型中综合考虑了路径损耗、阴影衰落和多径衰落等因素,实现了基于现场可编程门阵列（Field Programmable Gate Array,FPGA）的MIMO信道模拟器,并分析了硬件设计方案以及MIMO信道模拟的实现方法。实测结果表明,设计的MIMO信道模拟器可以模拟瑞利衰落、莱斯衰落以及阴影衰落等常见的信道衰落类型,能够应用于3GPP、COST-207等标准信道模型的复现。该模拟器可作为无线通信系统研究的测试设备,辅助通信系统研究的算法验证、方案优化以及性能分析。     

基于序贯蒙特卡罗滤波的MIMO快时变信道跟踪

高数据传输速率以及终端的高速移动,导致无线通信信道具有时间选择性与频率选择性两个特征.本文主要研究了基于训练序列的多输入多输出（MIMO）时变频率选择性衰落信道的估计与跟踪问题.首先,根据时变无线信道的动态性,将信道冲击响应近似看作一个低阶的自回归矢量过程（AR）,以便于进行时变信道的跟踪.接着在此模型的基础上,利用序贯蒙特卡罗滤波对MIMO通信系统中的双选择性信道进行了跟踪;跟踪过程中需要与信号检测交替进行,即在状态变量的预测和新息修正的中间要进行一次码元的检测,所采用的方法是极大似然序列检测,最后与扩展卡尔曼滤波作了比较.仿真结果表明,在信道噪声是非高斯的情形下,序贯蒙特卡罗滤波的跟踪性能更优越于扩展卡尔曼滤波.     

Low-Complexity Channel Estimation for Wireless Block Transmission Systems in Time-and Frequency-Selective Rayleigh Fading Channels

Mobility-induced Doppler spread and multipath propagation introduce the time- and frequency-selectivity (doubly selectivity) in fading channels. Based on the complex exponential basis expansion model (BEM) to approximate the doubly selective channel (DSC), a low-complexity channel estimation scheme for block transmission systems over DSC are developed in this paper. Using the developed scheme, the long data block is divided into a few short data subblocks in terms of the maximum normalized Doppler frequency and block length, and each subblock is performed to respective channel estimation. Thus the total calculation complexity is effectively decreased because the number of the BEM channel coefficients to estimate is greatly reduced for each sub-block. Moreover, by utilizing these channel estimation values to refit the true channel, we can obtain better channel estimation. Besides, the normalized mean square error (NMSE) expressions for the developed scheme and the existing scheme are derived in detail, respectively. Compared to the existing scheme, the proposed scheme has lower calculation complexity and superior performance. The simulation results verify the effectiveness of the developed scheme, and the theory values of the derived NMSE accord with corresponding simulation values.     

A new subspace method for blind estimation of selective MIMO‐STBC channels

In this paper, a new technique for the blind estimation of frequency and/or time‐selective multiple‐input multiple‐output (MIMO) channels under space‐time block coding (STBC) transmissions is presented. The proposed method relies on a basis expansion model (BEM) of the MIMO channel, which reduces the number of parameters to be estimated, and includes many practical STBC‐based transmission scenarios, such as STBC‐orthogonal frequency division multiplexing (OFDM), space‐frequency block coding (SFBC), time‐reversal STBC, and time‐varying STBC encoded systems. Inspired by the unconstrained blind maximum likelihood (UML) decoder, the proposed criterion is a subspace method that efficiently exploits all the information provided by the STBC structure, as well as by the reduced‐rank representation of the MIMO channel. The method, which is independent of the specific signal constellation, is able to blindly recover the MIMO channel within a small number of available blocks at the receiver side. In fact, for some particular cases of interest such as orthogonal STBC‐OFDM schemes, the proposed technique blindly identifies the channel using just one data block. The complexity of the proposed approach reduces to the solution of a generalized eigenvalue (GEV) problem and its computational cost is linear in the number of sub‐channels. An identifiability analysis and some numerical examples illustrating the performance of the proposed algorithm are also provided. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimation and equalization of doubly selective channels using known symbol padding

This paper considers the situation where users that experience high-mobility transmit data over frequency-selective channels, resulting in a doubly selective channel model (i.e., time- and frequency-selective channels) and this within the framework of Known Symbol Padding (KSP) transmission. KSP is a recently proposed block transmission technique where short sequences of known symbols acting as guard bands are inserted between successive blocks of data symbols. This paper proposes three novel channel estimation methods that allow for an accurate estimation of the time-varying transmission channel solely relying on the knowledge of the redundant symbols introduced by the KSP transmission scheme. The first method is a direct adaptive one while the others rely on a recently proposed model, the Basis Expansion Model (BEM), where the doubly selective channel is approximated with high accuracy using a limited number of complex exponentials. An important characteristic of the proposed methods is that they exploit all the received symbols that contain contributions from the training sequences and blindly filter out the contribution of the unknown surrounding data symbols. Besides these channel identification methods, the classical KSP equalizers are adapted to the context of doubly selective channels, which allows evaluation of the bit-error-rate (BER) performance of a KSP transmission system relying on the proposed channel estimation methods in the context of doubly selective channels. Simulation results show that KSP transmission is indeed a suitable transmission technique toward the delivery of high data rates to users experiencing a high mobility, when adapted KSP equalizers are used in combination with the proposed channel estimation methods.     

An Adaptive Channel Estimation Technique in MIMO OFDM Systems

In this paper, an adaptive channel estimation for MIMO OFDM is proposed. A set of pilot tones first are placed in each OFDM block, then the channel frequency response of these pilot tones are adaptively estimated by reeursive least squares （RLS） directly in frequency domain not in time domain. Then after the estimation of the channel frequency response of pilot tones, to obtain the channel frequency response of data tones, a new interpolation method based on DFT different from traditional linear interpolation method according to adjacent pilot tones is proposed. Simulation results show good performance of the technique.     

Iterative Estimation and Equalization for SCCP Over Doubly Selective Channels

In time varying channels, symbol recovery for single carrier cyclic prefix (SCCP) systems becomes complicated, because the orthogonality of channel frequency response (CFR) matrix is destroyed. In response, we propose a block turbo equalization algorithm in the time domain for SCCP to cope with channel time variations. In particular, the band structure of the channel time response (CTR) matrix is exploited to reduce the computational complexity of matrix inversion. In order to use this equalization scheme, accurate channel state information (CSI) must be available. Accordingly, we present a doubly selective channel estimation method for SCCP block transmissions with the aid of a Karhunen-Loeve basis expansion model (KL-BEM). In this method, the channel estimates are firstly obtained by using the cyclic prefix (CP) of each block, and then further refined by employing an expectation maximization (EM) based iterative algorithm. Combining the iterative estimator with the proposed equalizer naturally results in a doubly iterative receiver, the performance of which is shown to come close to the performance with perfect CSI.     

Optimal training for block transmissions over doubly selective wireless fading channels

High data rates give rise to frequency-selective propagation, whereas carrier frequency-offsets and mobility-induced Doppler shifts introduce time-selectivity in wireless links. To mitigate the resulting time- and frequency-selective (or doubly selective) channels, optimal training sequences have been designed only for special cases: pilot symbol assisted modulation (PSAM) for time-selective channels and pilot tone-assisted orthogonal frequency division multiplexing (OFDM) for frequency-selective channels. Relying on a basis expansion channel model, we design low-complexity optimal PSAM for block transmissions over doubly selective channels. The optimality in designing our PSAM parameters consists of maximizing a tight lower bound on the average channel capacity that is shown to be equivalent to the minimization of the minimum mean-square channel estimation error. Numerical results corroborate our theoretical designs.     

Frequency-selective multiple-input multiple-output channel estimation with transmitter non-linearities

The authors propose an algorithm based on the knowledge of training sequences to obtain an asymptotically unbiased estimator of non-linear multiple-input multiple-output (MIMO) channels, which involves the radio frequency front-end non-linearity and linear frequency selective MIMO channels. Although the impact of non-linearity in the transmitter side has been widely studied, most work on the channel estimation assumes linear channel models and ignores the non-linear effects. In this study, we develop a nonlinear channel estimator that can simultaneously estimate the linear MIMO channel model and non-linearity of the transmitter is developed. With these two sets of parameters, the non-linear channel model can be fully described. This channel estimation algorithm is implemented over an empirical MIMO channel model using an orthogonal frequency division multiplexing system.     

GMP-Based Channel Estimation for Single-Carrier Transmissions over Doubly Selective Channels

We present a graph-based channel estimation approach for SC-IFDE (single-carrier transmissions with iterative frequency domain equalization) without CP (cyclic prefix) over doubly selective channels using the recently developed Gaussian message passing (GMP) technique. A direct application of the GMP updating rules in the FFG (Forney-style factor graph) of the SC-IFDE system model incurs high complexity. Approximate updating rules are therefore developed to overcome this problem. The proposed GMP-based channel estimation approach has similar complexity as the low-complexity Kalman-filtering based frequency domain channel estimation approach in the literature, but significantly outperforms the latter due to its enhanced capability in capturing the time correlation information of doubly selective channels through bidirectional processing.