Blind Channel Identification Algorithm Research Articles

Robust blind identification algorithm in stationary noise environments

Blind channel identification aims to extract the channel impulse response with little prior statistical knowledge of the source signal. In this study, a novel blind channel identification algorithm is proposed based on second-order statistics. This algorithm utilises the special structure of the covariance matrix to identify the channel impulse responses. The proposed algorithm also outperforms the conventional blind identification algorithm with a low signal-to-noise ratio (SNR) and a few observations. Theoretically, the performance of this algorithm is independent of the SNR. This algorithm can be used in both white or coloured noise environments provided that the noise is stationary. This algorithm is robust to parameter estimation errors and has a simple and efficient implementation. Simulation examples are provided to demonstrate the superior performance of this algorithm.

Performance Comparison of High Order Moments Blind Channel Identification

In this paper, we compare different order of statistical moments to blindly estimate the propagation channel in an OFDM based wireless network. We first derive the theoretical expression of the channel estimation error and we compare it to simulation results in different scenarios. These simulation results show a good agreement with the theoretical expression. Furthermore, we show that blind channel identification algorithms, based on different moment's order, exhibit similar performance in terms of the Normalized Mean Square Error (NMSE) and in terms of the Bit Error Rate (BER). We conclude that the choice of the moment's order is uniquely based on the ambiguity solving stage in the algorithm. The proposed study shows that the choice of the 4 th moment is a trade-off between channel estimation performance and ambiguity solving complexity.

Distributed and recursive blind channel identification to sensor networks

In this paper, the distributed and recursive blind channel identification algorithms are proposed for single-input multi-output (SIMO) systems of sensor networks (both time-invariant and time-varying networks). At any time, each agent updates its estimate using the local observation and the information derived from its neighboring agents. The algorithms are based on the truncated stochastic approximation and their convergence is proved. A simulation example is presented and the computation results are shown to be consistent with theoretical analysis.

An Improved Subspace-Based Algorithm for Blind Channel Identification Using Few Received Blocks

In the last decade, several subspace-based algorithms for blind channel identification in zero-padded systems were introduced. These subspace-based methods are attractive because highly accurate estimates of the channel can be obtained by using only a few received blocks. However, they do not work when applied to the zero-padded orthogonal frequency division multiplexing systems (ZP-OFDM) with virtual carriers. In this paper, we propose two improvements on an earlier subspace-based blind channel estimation method for such systems. Firstly, we introduce a simple noise weighting approach. Unlike most of the earlier methods, the proposed weighting matrix is diagonal and it is independent of the channel noise variance and SNR. By doing so, the performance of previous work is significantly enhanced. Secondly, we extend the blind estimation method to ZP-OFDM systems with virtual carriers. Simulations are carried out to demonstrate the merits of the proposed method.

Blind Identification of Transmission Channel with the method of Higher-OrderCummulants

The modern telecommunication systems require very high transmission rates, in this context, the problem of channels identification is a challenge major. The use of blind techniques is a great interest to have the best compromise between a suitable bit rate and quality of the information retrieved.
 In this paper, we are interested to learn the algorithms for blind channel identification. We propose a hybrid method that performs a trade-off between two existing methods in order to improve the channel estimation.

Performance and Complexity Analysis of Blind FIR Channel Identification Algorithms Based on Deterministic Maximum Likelihood in SIMO Systems

We analyze two algorithms that have been introduced previously for Deterministic Maximum Likelihood (DML) blind estimation of multiple FIR channels. The first one is a modification of the Iterative Quadratic ML (IQML) algorithm. IQML gives biased estimates of the channel and performs poorly at low SNR due to noise induced bias. The IQML cost function can be “denoised” by eliminating the noise contribution: the resulting algorithm, Denoised IQML (DIQML), gives consistent estimates and outperforms IQML. Furthermore, DIQML is asymptotically globally convergent and hence insensitive to the initialization. Its asymptotic performance does not reach the DML performance though. The second strategy, called Pseudo-Quadratic ML (PQML), is naturally denoised. The denoising in PQML is furthermore more efficient than in DIQML: PQML yields the same asymptotic performance as DML, as opposed to DIQML, but requires a consistent initialization. We furthermore compare DIQML and PQML to the strategy of alternating minimization w.r.t. symbols and channel for solving DML (AQML). An asymptotic performance analysis, a complexity evaluation and simulation results are also presented. The proposed DIQML and PQML algorithms can immediately be applied also to other subspace problems such as frequency estimation of sinusoids in noise or direction of arrival estimation with uniform linear arrays.

Generalized Robust Multichannel Frequency-Domain LMS Algorithms for Blind Channel Identification

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

Generalized Robust Multichannel Frequency-Domain LMS Algorithms for Blind Channel Identification

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

Cross-Relation-Based Blind SIMO Identifiability in the Presence of Near-Common Zeros and Noise

The blind identification of single-input multiple-output (SIMO) systems is often performed by exploiting a cross-relation (CR) between channel pairs. It has been shown that this property allows an estimation of channel impulse responses up to a common gain factor if certain identifiability conditions are met. In this case, the estimated channels can be evaluated by a gain-compensated system distance known as normalized projection misalignment (NPM). Current algorithms for blind channel identification, however, suffer in the presence of insufficient channel diversity and observation noise. In this paper, we first demonstrate that in the absence of noise the CR identification error for channels with exact common zeros is given by a single-channel pole-zero transfer function. Next, we extend our analysis to the realistic case of near-common zeros and noise for which we show that the effective error can still be approximated by a common transfer function as long as the distance between the channel zeros remains below a signal-to-noise ratio-dependent threshold. A finite impulse response (FIR) modeling of the error then enables us to define a common-filter-error-compensated system distance, termed normalized filter-projection misalignment (NFPM), which establishes a natural extension to the NPM analysis. By finally considering realistic channels, which we blindly estimate with the adaptive multichannel least mean-square (MCLMS) algorithm, we demonstrate that the NFPM reliably reaches the noise floor, confirming that the effective error can be approximated by a common FIR filter.

Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

We present an adaptive algorithm for blind identification and equalization of single-input multiple-output (SIMO) FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares (Bi-LS) subspace tracking method. The new algorithm requires only a computational complexity of O(md2) at each iteration, or even as low as O(md) if only equalization is necessary, where m is the dimension of the received data vector (or the row rank of channel matrix) and d is the dimension of the signal subspace (or the column rank of channel matrix). To overcome the shortcoming of the back substitution, an inverse QR iteration algorithm for subspace tracking and channel equalization is also developed. The inverse QR iteration algorithm is well suited for the parallel implementation in the systolic array. Simulation results are presented to illustrate the effectiveness of the proposed algorithms for the channel identification and equalization.

Weighted Semi-Blind Channel Identification by Cross Relation Method

Problem statement: The bandwidth efficiency of many communication systems could be improved if the transmission channel was estimated blindly without resort to training sequences. GMSK was a spectrum-efficient modulation scheme and it was adopted as the modulation standard of GSM systems. Approach: However, because of its phase modulation, Gaussian filtering and partial response signalling properties, Results: The simulation results showed great potential of semi-blind identification algorithms, since we used no extra antenna or over-sampling the received signal. Conclusion: GMSK was not a linear modulation. Linear approximation of the GMSK signal made the blind equalization system model applicable for GSM. In the sequel, a linear approximation of GMSK signals was presented and a blind GSM and semi-blind channel identification algorithm based on the cross relation method was suggested.

A new adaptive blind channel identification algorithm

This paper addresses the blind identification of single-input multiple-output (SIMO) finite-impulse-response (FIR) systems. We first propose a new adaptive algorithm for the blind identification of SIMO FIR systems. Then, its convergence property is analyzed systematically. It is shown that under some mild conditions, the proposed algorithm is guaranteed to converge in the mean to the true channel impulse responses in both noisy and noiseless cases. Simulations are carried out to demonstrate the theoretical results.

Robust multichannel least mean square-type algorithms with fast decaying transient for blind identification of acoustic channels

The multichannel least mean square (MCLMS) is an attractive and effective algorithm for blind channel identification in the noise-free case. Some recent studies show that the performance of the MCLMS algorithm significantly deteriorates in a noisy environment, that is, the blind MCLMS solution does not remain collinear with the channel vector. Therefore the authors propose non-conventional technique that helps the MCLMS algorithm converge to a novel steady-state solution that is a weighted combination of all the eigenvectors, with the weight profile inversely proportional to the eigenvalues. The improved performance of the proposed solution is verified both analytically and numerically. The algorithm is then optimised by introducing an adaptive step size that ensures fast decay of the transient response, giving stability as well as rapid convergence to the final solution. The authors then apply the proposed technique to different variants of the MCLMS algorithm, including frequency-domain implementations, to achieve a noise-robust performance. Computer simulations are presented that show improved performance of the proposed algorithms for blind identification of both acoustic and random channels with noise.

Blind channel identification algorithms based on the Parafac decomposition of cumulant tensors: The single and multiuser cases

In this paper, we exploit the symmetry properties of 4th-order cumulants to develop new blind channel identification algorithms that utilize the parallel factor (Parafac) decomposition of cumulant tensors by solving a single-step (SS) least squares (LS) problem. We first consider the case of single-input single-output (SISO) finite impulse response (FIR) channels and then we extend the results to multiple-input multiple-output (MIMO) instantaneous mixtures. Our approach is based on 4th-order output cumulants only and it is shown to hold for certain underdetermined mixtures, i.e. systems with more sources than sensors. A simplified approach using a reduced-order tensor is also discussed. Computer simulations are provided to assess the performance of the proposed algorithms in both SISO and MIMO cases, comparing them to other existing solutions. Initialization and convergence issues are also addressed.

Subspace algorithm for blind channel identification and synchronization in single-carrier block transmission systems

A blind channel identification and synchronization method for single-carrier block transmission system over frequency-selective fading channel is proposed. We employ cyclic prefix (CP) insertion to introduce the redundancy at the transmitter side. As a result, the channel impulse response (CIR) could be estimated in a blind fashion by means of subspace algorithm based on second-order statistics through the exploitation of the redundancy. The condition for identifying CIR along with the proof of uniqueness of CIR estimate is provided regardless of CP length relative to channel order as well as data frame size. We further develop a sorting approach to avert timing offset issue in block synchronization. Numerical results presented performance comparison with orthogonal frequency-division multiplexing (OFDM) systems in terms of bit error rate (BER) and channel estimation error. We also compare with traditional zero-padding OFDM systems in terms of the failure rate of block synchronization.

Energy constrained frequency-domain normalized LMS algorithm for blind channel identification

This paper deals with the blind adaptive identification of single-input multi-output (SIMO) finite impulse response acoustic channels from noise-corrupted observations. The normalized multichannel frequency-domain least-mean-squares (NMCFLMS) algorithm [1] is known to be a very effective and efficient technique for identification of such channels when noise effects can be ignored. It, however, misconverges in presence of noise [2]. In this paper, we present an analysis of noise effects on the NMCFLMS algorithm and propose a novel technique for ameliorating such misconvergence characteristics of the NMCFLMS algorithm for blind channel identification (BCI) with noise by attaching a spectral constraint in the adaptation rule. Experimental results demonstrate that the robustness of the NMCFLMS algorithm for BCI can be significantly improved using such a constraint.

A Generalized Algorithm for Blind Channel Identification with Linear Redundant Precoders

It is well known that redundant filter bank precoders can be used for blind identification as well as equalization of FIR channels. Several algorithms have been proposed in the literature exploiting trailing zeros in the transmitter. In this paper we propose a generalized algorithm of which the previous algorithms are special cases. By carefully choosing system parameters, we can jointly optimize the system performance and computational complexity. Both time domain and frequency domain approaches of channel identification algorithms are proposed. Simulation results show that the proposed algorithm outperforms the previous ones when the parameters are optimally chosen, especially in time-varying channel environments. A new concept of generalized signal richness for vector signals is introduced of which several properties are studied.

Blind multiuser detection for V-BLAST coded multicarrier CDMA system

Focusing on the space-time coded multiuser mobile communication systems in the frequency-selective fading environment, this paper proposes a Vertical Bell labs LAyered Space-Time (V-BLAST) coded Multicarrier Code-Division Multiple-Access (MC-CDMA) scheme and its blind channel identification algorithm. This algorithm employs an ESPRIT-like method and the singular value decomposition, and the channels between every transmit antenna of every user and every receive antenna of the base station are blindly estimated with a closed-form solution. Based on it, an equivalent Minimum Mean-Squared Error (MMSE) time-domain multiuser detector is derived. Moreover, the proposed scheme exploits the precoding in the transmitter in order to eliminate the constraint of more receive antennas than transmit ones, required by most conventional V-BLAST codec schemes. Computer simulation results demonstrate the validity of this proposed scheme.

A Subspace Blind Identification Algorithm with Reduced Computational Complexity--Colored Noise Case--

We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.

Blind channel estimation and equalization in wireless sensor networks based on correlations among sensors

In densely deployed wireless sensor networks, signals of adjacent sensors can be highly cross-correlated. This paper proposes to utilize such a property to develop efficient and robust blind channel identification and equalization algorithms. Blind equalization can be performed with complexity as low as O(N/spl tilde/), where N/spl tilde/ is the length of equalizers. Transmissions can be more power and bandwidth efficient in multipath propagation environment, which is especially important for wideband sensor networks such as those for acoustic location or video surveillance. The cross-correlation property of sensor signals and the finite sample effect are analyzed quantitatively to guide the design of low duty-cycle sensor networks. Simulations demonstrate the superior performance of the proposed method.