A novel blind estimation algorithm

In this paper, we propose a cumulant-based blind signal estimation algorithm for estimating the channel matrix in an n-sensor m-source system. The only available information is the output of the n sensors. The algorithm first deduces the number of sources, which may be greater than or equal to the number of sensors, from the output cumulant matrix. Then, by suitably arranging the elements within that matrix, the entries of the original channel matrix are estimated row by row. Simulations results are given to illustrate the performance of the algorithm     

Asymptotic analysis of blind multiuser detection with blind channel estimation

The analytical performance of the subspace-based blind linear minimum mean-square error (MMSE) multiuser detection algorithm in general multipath multi-antenna code-division multiple-access (CDMA) systems is investigated. In blind multiuser detection, the linear MMSE detector of a given user is estimated from the received signals, based on the knowledge of only the spreading sequence of that user. Typically, the channel of that user must be estimated first, based on the orthogonality between the signal and noise subspaces. An asymptotic limit theorem for the estimate of the blind linear detector (when the received signal sample size is large) is obtained, based on which approximate expressions of the average output signal-to-inference plus noise ratios (SINRs) and bit error rates (BERs) for both binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) modulations are given. Corresponding results for group-blind multiuser detectors are also obtained. Examples are provided to demonstrate the excellent match between the theory developed in this paper and the simulation results.     

Waveform-preserving blind estimation of multiple independentsources

The problem of blind estimation of source signals is to estimate the source signals without knowing the characteristics of the transmission channel. It is shown that the minimum-variance unbiased estimates can be obtained if and only if the transmission channel can be identified blindly. It is shown that the channel can be blindly identified if and only if there is not more than one Gaussian source. This condition suggests that waveform-preserving blind estimation can be achieved over a wide range of signal processing applications, including those cases in which the source signals have identical nonGaussian distributions. The constructive proof of the necessary and sufficient condition serves as a foundation for the development of waveform-preserving blind signal estimation algorithms. Examples are presented to demonstrate the applications of the theoretical results     

Error propagation in blind sequence estimation

We demonstrate how error propagation may occur with blind sequence estimation based on the Viterbi algorithm (BSE-VA). We further show that differential encoding can be used to alleviate this error propagation in blind sequence estimation. In addition, we compare the BSE-VA with a much simpler differential correlation approach (DCA) method for differentially encoded input symbols     

Blind channel estimation via combining autocorrelation and blind phase estimation

Symbol spaced blind channel estimation methods are presented which can essentially use the results of any existing blind equalization method to provide a blind channel estimate of the channel. Blind equalizer's task is reduced to only phase equalization (or identification) as the channel autocorrelation is used to obtain the amplitude response of the channel. Hence, when coupled with simple algorithms such as the constant modulus algorithm (CMA) these methods at baud rate processing provide alternatives to blind channel estimation algorithms that use explicit higher order statistics (HOS) or second-order statistics (subspace) based fractionally-spaced/multichannel algorithms. The proposed methods use finite impulse response (FIR) filter linear receiver equalizer or matched filter receiver based infinite impulse response+FIR linear cascade equalizer configurations to obtain blind channel estimates. It is shown that the utilization of channel autocorrelation information together with blind phase identification of the CMA is very effective to obtain blind channel estimation. The idea of combining estimated channel autocorrelation with blind phase estimation can further be extended to improve the HOS based blind channel estimators in a way that the quality of estimates are improved.     

Channel estimation standard and adaptive blind equalization

Presents a novel concept of channel estimation standard (CES) and applies a new CES error criterion to the process of an adaptive blind equalization. It is shown that the establishment of the CES contributes to the development of a practical communication scheme for approaching to the capacity of a high SNR band-limited channel without using a preamble signal training     

OFDM blind carrier offset estimation: ESPRIT

In orthogonal frequency-division multiplex (OFDM) communications, the loss of orthogonality due to the carrier-frequency offset must be compensated before discrete Fourier transform-based demodulation can be performed. This paper proposes a new carrier offset estimation technique for OFDM communications over a frequency-selective fading channel. We exploit the intrinsic structure information of OFDM signals to derive a carrier offset estimator that offers the accuracy of a super resolution subspace method, ESPRIT     

Closed-form blind symbol estimation in digital communications

We study the blind symbol estimation problem in digital communications and propose a novel algorithm by exploiting a special data structure of an oversampled system output. Unlike most equalization schemes that involve two stages-channel identification and channel equalization/symbol estimation-the proposed approach accomplishes direct symbol estimation without determining the channel characteristics. Based on a deterministic model, the new method can provide a closed-form solution to the symbol estimation using a small set of data samples, which makes it particularly suitable for wireless applications with fast changing environments. Moreover, if the symbols belong to a finite alphabet, e.g., BPSK or QPSK, our approach can be extended to handle the symbol estimation for multiple sources. Computer simulations and field RF experiments were conducted to demonstrate the performance of the proposed method. The results are compared to the Cramer-Rao lower bound of the symbol estimates derived in this paper     

Robust estimation approach for blind denoising.

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.     

Cluster-based distributed consensus

In this paper, we incorporate clustering techniques into distributed consensus algorithms for faster convergence and better energy efficiency. Together with a simple distributed clustering algorithm, we design cluster-based distributed consensus algorithms in forms of both fixed linear iteration and randomized gossip. The time complexity of the proposed algorithms is presented in terms of metrics of the original and induced graphs, through which the advantage of clustering is revealed. Our cluster-based algorithms are also shown to achieve an Ω(log n) gain in message complexity over the standard ones.     

通信信号的单通道盲分离和码元序列盲估计

针对被单根天线接收的多个通信信号的单通道盲分离问题,提出了一种新算法.该算法利用各个通信信号的周期性差异和波形差异,应用过采样技术和高阶统计量分析技术来构造适用于独立分量分析的多通道混合模型,从而将多信号盲分离问题转化成波形和码元序列的估计问题.仿真证实了新算法的可行性和优越性.     

Asymptotically optimal blind estimation of multichannel images.

Optimal estimation of a two-dimensional (2-D) multichannel signal ideally decorrelates the data in both channel and space and weights the resulting coefficients according to their SNR. Many scenarios exist where the required second-order signal and noise statistics are not known in which the decorrelation is difficult or expensive to calculate. An asymptotically optimal estimation scheme proposed here uses a 2-D discrete wavelet transform to approximately decorrelate the signal in space and the discrete Fourier transform to decorrelate between channels. The coefficient weighting is replaced with a wavelet-domain thresholding operation to result in an efficient estimation scheme for both stationary and nonstationary signals. In contrast to optimal estimation, this new scheme does not require second-order signal statistics, making it well suited to many applications. In addition to providing vastly improved visual quality, the new estimator typically yields signal-to-noise ratio gains 12 dB or higher for hyperspectral imagery and functional magnetic resonance images.     

Subspace-based blind and semi-blind channel estimation for OFDMsystems

This paper proposes a new blind channel estimation method for orthogonal frequency division multiplexing (OFDM) systems. The algorithm makes use of the redundancy introduced by the cyclic prefix to identify the channel based on a subspace approach. Thus, the proposed method does not require any modification of the transmitter and applies to most existing OFDM systems. Semi-blind procedures taking advantage of training data are also proposed. These can be training symbols or pilot tones, the latter being used for solving the intrinsic indetermination of blind channel estimation. Identifiability results are provided, showing that in the (theoretical) situation where channel zeros are located on subcarriers, the algorithm does not ensure uniqueness of the channel estimation, unless the full noise subspace is considered. Simulations comparing the proposed method with a decision-directed channel estimator finally illustrates the performance of the proposed algorithm     

OFDM系统中一种新型盲载波频偏估计

正交频分复用(OFDM)是一种能够有效地抗频率选择性衰落的技术。由于载波频偏的存在会引起载波间干扰,导致系统性能的严重下降。在分析载波频偏对系统影响的基础上,提出了一种联合译码的新型盲频偏估计算法。该算法只需要一个OFDM符号,将BICM译码的硬判决反馈信息作为虚拟训练序列,利用该反馈信号与接收信号的相关性进行频偏估计。通过BICM迭代译码,更新译码反馈信息,提高频偏估计精度。仿真结果表明,提出的算法经过3次迭代,可以有效地进行频偏估计。     

Asymptotically near-optimal blind estimation of multipath CDMA channels

In this paper, correlation matching techniques are applied to estimate multipath code division multiple access (CDMA) channels. We arrange unknown multipath parameters for each of J active users in a vector. Then, the output correlation matrix is parameterized by J unknown rank one matrices, with each one formulated from the corresponding channel vector. This correlation matrix is further compared with its sample average. The resulting error can be first minimized to obtain unbiased estimates of J unknown rank one matrices in closed forms. Thus, our estimator for each channel vector is derived by singular value decomposition (SVD) on the associated rank one matrix within a scalar ambiguity. It turns out that the performance of our estimator can be improved by introducing an asymptotically optimal weighting matrix in our cost function. This weighting matrix can be estimated directly from data samples only with a small penalty on the asymptotic performance. The asymptotic covariance of our estimator is also derived and can be compared with the Cramer-Rao lower bound, both in closed forms. Simulation results show the applicability of the proposed methods and consistency with our theoretical analysis     

Adaptive blind channel estimation by least squares smoothing

A least squares smoothing (LSS) approach is presented for the blind estimation of single-input multiple-output (SIMO) finite impulse response systems. By exploiting the isomorphic relation between the input and output subspaces, this geometrical approach identifies the channel from a specially formed least squares smoothing error of the channel output. LSS has the finite sample convergence property, i.e., in the absence of noise, the channel is estimated perfectly with only a finite number of data samples. Referred to as the adaptive least squares smoothing (A-LSS) algorithm, the adaptive implementation has a high convergence rate and low computation cost with no matrix operations. A-LSS is order recursive and is implemented in part using a lattice filter. It has the advantage that when the channel order varies, channel estimates can be obtained without structural change of the implementation. For uncorrelated input sequence, the proposed algorithm performs direct deconvolution as a by-product     

Data-efficient blind OFDM channel estimation using receiver diversity

We investigate non data-aided channel estimation for cyclically prefixed orthogonal frequency division multiplexing (OFDM) systems. By exploiting channel diversity using only two receive antennas, a blind deterministic algorithm is proposed. Identifiability conditions are derived that guarantee the perfect channel retrieval in the absence of noise. In the presence of noise, the proposed method has the desired property of being data efficient-only a single OFDM block is needed to achieve good estimation performance for a wide range of SNR values. The algorithm is also robust to input symbols as it does not have any restriction on the input symbols with regard to their constellation or their statistical properties. In addition, this diversity-based algorithm is computationally efficient, and its performance compares favorably to most existing blind algorithms.     

Periodic variation method for blind symbol rate estimation

In order to obtain unknown symbol rate of incoming signal at a receiver, in this paper, cyclostationary features of linear digitally modulated signals are exploited by proposed periodic variation method. A low complexity but highly accurate symbol rate estimation technique is obtained. The proposed method is based on a superposed epoch analysis over autocorrelations obtained blindly in different sampling frequencies. The obtained autocorrelations are analyzed in the frequency domain, and it is seen that there are large oscillations when the autocorrelation is obtained around the symbol rate. Then, a superposed epoch analysis is developed in order to estimate symbol rate based of the periodic variations on the frequency responses of autocorrelations. The proposed algorithm is quite accurate in the noisy environment because the noise is having no frequency component after taking Fourier transform of autocorrelations in all sampling rates, and this feature is also valid for the offset frequency that the purposed estimation is not affected by offset frequency. Thus, a successful blind symbol rate estimation algorithm is obtained, and it performs much better error performance than those using the well‐known cyclic correlation based symbol rate estimations, as it is proven by the obtained performances presented in the paper. Copyright © 2013 John Wiley & Sons, Ltd.     

信号排列组合的子空间快速盲信道估计算法

针对传统子空间算法进行MIMO-OFDM信道估计时,存在收敛速度慢、鲁棒性差的问题,提出一种基于收发信号排列组合的改进子空间盲信道估计算法。该算法通过对接收端信号进行有选择地取舍,并利用取舍后接收端数据块的无关联性,对信道矩阵进行类Toeplitz扩展,然后对收发两端信号进行相应的排列组合,增大发送数据块矩阵的秩,从而实现算法的快速收敛。理论分析与仿真结果表明,与传统的子空间算法相比,本文算法在复杂度略有提高的条件下能够实现快速收敛。      

Recursive blind symbol estimation of convolutionally codedcochannel signals

A recursive blind equalizer is presented that directly estimates the transmitted symbols of multiple cochannel signals in the presence of ISI. The algorithm exploits shift structure present in the data model and the finite alphabet property of the signals. The proposed method possesses a separation property that allows the symbol sequences for each user to be estimated independently of the others. Problematic issues surrounding unknown and mismatched channel lengths for the cochannel users can be handled effectively in the recursive equalizer. Additionally, if the cochannel signals are encoded prior to transmission, we show how the code structure can be incorporated into the recursive equalizer to improve its performance