Novel Semi-blind Channel Estimation Schemes for Rayleigh Flat Fading MIMO Channels

In this paper, we propose two novel semi-blind channel estimation techniques based on QR decomposition for Rayleigh flat fading Multiple Input Multiple output (MIMO) channel using various pilot symbols. In the first technique, the flat-fading MIMO channel matrix H can be decomposed as an upper triangular matrix R and a unitary rotation matrix Q as H = RQ. The matrix R is estimated blindly from only received data by using orthogonal matrix triangularization based house holder QR decomposition, while the optimum rotation matrix Q is estimated exclusively from pilot based Orthogonal Pilot Maximum Likelihood Estimator (OPML) algorithm. In the second technique, joint semi-blind channel and data estimation is performed using QR decomposition based Least Square (LS) algorithm. Simulations have taken under 4-PSK data modulation scheme for two transmitters and six receiver antennas using various training symbols. Finally, these two new techniques compare with Whitening Rotation (WR) based semi-blind channel estimation technique and results shows that those new techniques achieve very nearby performance with low complexity compare to Whitening rotation based technique. Also first technique with perfect R outperforms Whitening Rotation based technique.

Semi-Blind Channel Estimation Algorithm for OFDM Systems

A semi-blind channel estimation algorithm based on subspace approach for orthogonal frequency division multiplexing（OFDM） systems over the frequency-selective channel is proposed. A linear preeoding is applied on each block before the IFFT operation and a low-rank structure is created in the received signal. Then subspace properties can be exploited to identify the channel up to a scalar ambiguity. The residual scalar ambiguities eliminated by inserting pilots into data stream. Simulation results illustrate the performance of the proposed semi-blind algorithm.

Adaptive Bit Loading Scheme with Semi-Blind Channel Estimation for OFDMSystems

An adaptive bit loading and power-allocation scheme is proposed in order to augment the performance of the system based on orthogonal frequency division multiplexing （OFDM）, which is based on the maximum power margin. Coinciding with the adaptive loading scheme, a semi-blind channel estimation algorithm using subspace decomposition method is proposed, which uses the information in the cyclic prefix. An initial channel state information is estimated by using the training sequences with the method of interpolation filtering. The proposed adaptive scheme is simulated on an OFDM wireless local area network（WLAN） system in a time-varying channel. The performance is compared to the constant loading scheme.

New subspace algorithm for blind channel estimation in OFDM systems

In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.

Low-complexity fractional phase estimation for totally blind channel estimation

To remove the scalar ambiguity in conventional blind channel estimation algorithms, totally blind channel estimation （TBCE） is proposed by using multiple constellations. To estimate the unknown scalar, its phase is decomposed into a fractional phase and an integer phase. However, the maximum-likelihood （ML） algorithm for the fractional phase does not have closed-form solutions and suffers from high computational complexity. By ex- ploring the structures of widely used constellations, this paper proposes a low-complexity fractional phase estimation algorithm which requires no exhaustive search. Analytical expressions of the asymptotic mean squared error （MSE） are also derived. The theo- retical analysis and simulation results indicate that the proposed fractional phase estimation algorithm exhibits almost the same performance as the ML algorithm but with significantly reduced computational burden.

CRAM(?)R-RAO BOUND FOR BLIND CHANNEL ESTIMATION IN COMPLEX-VALUED CHANNEL

Compared with the traditional channel estimation methods, blind channel estimation methods can increase the bandwidth efficiency of the systems, but their precision is low and they converge slowly. In this paper, the Cramér-Rao Bound （CRB） for blind channel estimation in complex-valued Single-Input Multiple- Output （SIMO） channel is derived. In the simulations, the correctness of the CRB is validated and some channel estimation methods are evaluated by using the CRB.

Blind channel estimation for redundant precoded OFDM systems

Considering that channel estimation can play a crucial role in coherent detection of the information symbols in each data block, a blind channel estimation approach is proposed for redundant precoded orthogonal frequency-division multiplexing （OFDM） systems. A redundant linear frequency-domain precoder is applied to each pair of blocks before they enter the OFDM system. Because of the introduced structure, the frequency-selective channel can be identified at the receiver based on autocorrelation operations, singular value decomposition （SVD）, and by resolving the scalar ambiguity. The proposed channel estimation method has low computational complexity and requires no prior statistical information on channel or noise. And the proposed blind method has high spectral efficiency owing to exploiting no training sequence. Computer simulations confirm that this proposed blind channel estimation method can identify the frequency-selective channels perfectly and obtain a good performance.

Subspace-Based Blind Channel Estimation Method for MIMO-OFDM Systems

A subspace-based blind channel estination algo rithm for MIMO-OFDM systems is proposed. This algorithm exploits the cyclostationarity introduced by cyclic prefix of OFDM to estimate the channel parameters. The proposed new algorithm is found to be outperforming the other algorithm with respect to convergence rate and achievable mean square error and robustness to channel order over determination.

BLIND CHANNEL ESTIMATION IN DELAY DIVERSITY FOR FREQUENCY SELECTIVE CHANNELS

Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time with in whichdata may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.

Blind channel estimation through redundant linear precoding for OFDM systems

A novel approach of blind channel estimation through redundant linear precoding for orthogonal fre-quency-division multiplexing （OFDM） is proposed. A redundant linear frequency-domain preceder is ap-plied to each pair of blocks before they enter the OFDM system. With the aid of the introduced structure, the frequency-selective channel can be identified at the receiver through auto-correlation operations, sin-gular value decomposition （SVD） and scalar ambiguity resolution. The proposed blind channel estimation method has low computation complexity and requires no prior statistical information of channel or noise. The redundant linear frequency-domain precoder is employed to identify the frequency-selective fading channels. And the proposed blind channel estimation method has high spectral efficiency because it re-quires no training sequence. Computer simulations have proved that this proposed blind channel estima- tion method can identify the frequency-selective channels perfectly and have a good performance.

BLIND CHANNEL ESTIMATION OF SPACE-TIME FREQUENCY-SHIFT KEYING

The decoupled coherent Maximum Likelihood (ML) detection algorithm presented in this letter can sharply reduce the complexity of the receiver as well as provide better error performance under the precondition that channel should be estimated first. Considering the bandwidth inefficiency of Frequency Shift Keying (FSK), the acquisition of channel state information through training sequences will further decrease the transmission efficiency. This letter presents a blind channel estimation algorithm based on noise subspace theory which can acquire channel information without any training symbols. The simulation shows that the algorithm brings about fewer channel estimation errors while the frequency efficiency can be increased.

ADAPTIVE BLIND ESTIMATION ALGORITHM FOR OFDM-MIMO RADIO SYSTEMS OVER MULTIPATH CHANNELS

This paper investigates the blind algorithm for channel estimation of Orthogonal Frequency Division Multiplexing-Multiple Input Multiple Output (OFDM-MIMO) wireless communication system using the subspace decomposition of the channel received complex baseband signals and proposes a new two-stage blind algorithm. Exploited the second-order cyclostationarity inherent in OFDM with cyclic prefix and the characteristics of the phased antenna, the practical HIPERLAN/2 standard based OFDM-MIMO simulator is established with the sufficient consideration of statistical correlations between the multiple antenna channels under wireless wideband multipath fading environment, and a new two-stage blind algorithm is formulated using rank reduced subspace channel matrix approximation and adaptive Constant Modulus (CM)criterion. Simulation results confirm the theoretical analysis and illustrate that the proposed algorithm is capable of tracking matrix channel variations with fast convergence rate and improving acceptable overall system performance over various common wireless and mobile communication links.

A Novel Blind Channel Estimation for a 2x2 MIMO System

A novel blind channel estimation method based on a simple coding scheme for a 2 by 2 multiple input multiple output (MIMO) system is described. The proposed algorithm is easy to implement in comparison with conventional blind estimation algorithms, as it is able to recover the channel matrix without performing singular value decomposition (SVD) or eigenvalue decomposition (EVD). The block coding scheme accompanying the proposed estimation approach requires only a block encoder at the transmitter without the need of using the decoder at the receiver. The proposed block coding scheme offers the full coding rate and reduces the noise power to half of its original value. It eliminates the phase ambiguity using only one additional pilot sequence.

Equivalent Time-Domain Representation and Uplink Channel Estimator for Multicarrier CDMA Systems

Many blind channel estimation methods have been proposed for direct sequence (DS) code-division multiple access (CDMA) systems, so we can certainly use them to estimate the finite impulse response (FIR) channel for the multi-carrier (MC-) CDMA system. In this paper, the MC-CDMA system is interpreted as an equivalent time-domain DS-CD-MA system with specific spreading codes. Then, an equivalently time-domain blind channel estimator is derived for the uplink MC-CDMA, which is based on second-order statistics of the received data. By exploiting singular value decomposition (SVD) and the finite alphabet property of transmitted symbols, the time-domain channel impulse response (CIR) for the uplink MC-CDMA can be accurately identified. Computer simulations illustrate both the validity and the overall performance of the proposed estimator.

基于参数估计和Kalman滤波的单通道盲源分离算法

针对存在频谱混叠通信信号的单通道盲源分离(single channel blind source separation,SCBSS)问题,提出一种基于参数估计和Kalman滤波的SCBSS算法。首先,针对根多重信号分类(root multiple signal classification,Root-MUSIC)算法在相近载频估计方面的局限性,提出一种自适应的Root-MUSIC算法,对接收到的盲混合信号的源信号数目和载频进行估计;其次,将Kalman滤波的思想引入到SCBSS算法中,根据估计得到的源信号参数构造信号模型,将其作为Kalman滤波系统的观测向量,执行“时间更新”和“测量更新”两个过程,得到源信号的最佳估计,实现单通道盲源分离。仿真结果表明,所提算法能够有效地从存在频谱混叠的单路接收信号中准确地分离出多路源信号,比传统的算法分离精度高,运算速度快。

Joint Semi-Blind Channel Estimation and Data Sequence Detection of OFDM Systems

In this paper, a joint semi blind channel estimation and data sequence detection of OFDM systems over the multipath frequency selective fading channels is proposed and investigated. The basic idea of the algorithm is to first use the correlation among adjacent subchannels in the frequency domain to estimate the channel's parameters with an AutoRegressive (AR) process based on the decision directed estimation principle, and then to search the ML solution using the Viterbi algorithm. The algorithm realizes on line channel estimation and data detection of OFDM signals without any periodic pilot signal and statistic information about the channels, so it is suited to high data rate transmission over slow fading channels in the coming generation mobile communication systems.

Optimization on semi-blind channel estimation for MIMO-OFDM systems

An orthogonal frequency division multiplexing （OFDM） is one of the effective techniques used in wireless communication. In OFDM systems, channel impairments due to multipath dispersive spreading can cause deep fades in wireless channels. Thus, the OFDM receiver requires channel state information when coherent detection is involved. Therefore, to overcome the impact of channel fades good channel estimation （CE） methods are needed in OFDM systems. And one of these CE methods is a semi-blind CE. However, the semi-blind method requires a large number of processing operations. In order to avoid the high computing complexity of the existing method, scaled least square （SLS） technique is applied to improve the performance of the semi-blind channel estimator which require less knowledge of the channel second-order statistics and have better performance than the least square （LS） which used in semi-blind CE. Simulation results shows, this proposed method of semi-blind CE has the capacity of elevating CE performance in multiple-input multiple-output （MIMO） OFDM systems.

Orthogonal procrustes based semi-blind MIMO channel estimation under space-time block code transmissions

This work investigates a novel semi-blind channel estimation for multiple-input multiple-output （MIMO） space-time block coding （STBC） systems. Algorithms for channel estimation based on whitening-rotation （WR） decomposition that provides a combined quality and spatial scalability is utilized. Using a space-time code-constrained input design, our approach exploits the orthogonality of the signal and noise subspaces in conjunction with orthogonal procrustes （OP） technique to obtain an accurate estimate of the unitary rotation matrix and, consequently, of the channel parameters. Unitary rotation matrices are parameterized a much fewer number of parameters, and significant estimation gains can then be achieved by estimation of such orthogonal matrices. Furthermore, the proposed semi-blind MIMO channel estimation approach is conducted to reduce the complexity of system design when the number of the receive antennas is no less than the number of transmit antennas. Computer simulations are conducted to corroborate the effectiveness of the proposed channel estimation, and they demonstrate the improved performance compared to the existing training-based estimation.

基于聚类的盲信道估计算法研究与优化

正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)在现代通信系统中发挥着重要的作用,而信道估计是OFDM系统接收机的关键环节。针对传统导频信道估计算法需要大量的导频开销,降低信道带宽利用率,以及已有的盲信道估计算法估计性能差且算法复杂度高等不足,该文提出了一种基于改进聚类算法的盲信道估计算法。该算法在系统接收端把接收到信号看作一幅时频二维平面,通过使用时频窗口以迭代方式遍历该平面,在遍历过程中用改进的K-means算法对时频窗口内的符号数据做聚类分析,并根据符号先验信息以及信道时频相关性实现信道估计与均衡的目的。通过计算机仿真实验结果表明,改进K-means的盲信道估计算法的误比特率整体上低于基于简单线性预编码的盲估计算法,当信噪比大于10 dB时,改进K-means的盲信道估计算法的误比特率比子空间方法更低。在高信噪比情况下估计性能与最小均方误差(Minimum Mean Square Error,MMSE)估计算法相当。因此,相较于已有的盲信道估计算法,改进K-means的盲信道估计算法提高了估计精度,降低了算法复杂度。

基于信道估计的多通道通信信号盲分离方法

针对多通道通信信号盲源分离效果差,提出基于信道估计的多通道通信信号盲分离方法。根据信号频率、相位、斜率等特征建立信号通信模型,利用基扩展模型、傅里叶变换获取接收信号;使用联合函数、目标函数确定信号寻优参数,按照二进制算法编码信号各分量,结合随机函数生成初始种群,并做遗传处理,获取初始参数值,通过顶点函数搜寻出最优参数,确定源信号组成成分并重构,最终得到源信号,实现多通道通信信号盲分离。实验结果表明,所提方法信噪比为14.2 dB,失真比在9.6 dB左右,并且适用于多通道通信信号盲分离,该方法具备可行性和有效性。