Adaptive solution for blind identification/equalization using deterministic maximum likelihood (original) (raw)
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IEEE Transactions on Signal Processing, 2000
A (semi)deterministic maximum likelihood (DML) approach is presented to solve the joint blind channel identification and blind symbol estimation problem for single-input multiple-output systems. A partial prior on the symbols is incorporated into the criterion which improves the estimation accuracy and brings robustness toward poor channel diversity conditions. At the same time, this method introduces fewer local minima than the use of a full prior (statistical) ML. In the absence of noise, the proposed batch algorithm estimates perfectly the channel and symbols with a finite number of samples. Based on these considerations, an adaptive implementation of this algorithm is proposed. It presents some desirable properties including low complexity, robustness to channel overestimation, and high convergence rate.
Unbiased blind adaptive channel identification and equalization
IEEE Transactions on Signal Processing, 2000
The blind adaptive equalization and identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by sensor arrays or time oversampling, leading to the so-called second-order algebraic/statistical techniques. The prediction error method is one of them, perhaps the most appealing in practice, due to its inherent robustness to ill-defined channel lengths as well as for its simple adaptive implementation. Unfortunately, the performance of prediction error methods is known to be severely limited in noisy environments, which calls for the development of noise (bias) removal techniques. We present a low-cost algorithm that solves this problem and allows the adaptive estimation of unbiased linear predictors in additive noise with arbitrary autocorrelation. This algorithm does not require the knowledge of the noise variance and relies on a new constrained prediction cost function. The technique can be applied in other noisy prediction problems. Global convergence is established analytically. The performance of the denoising technique is evaluated over GSM test channels.
Blind Channel Estimation and Equalization
International Journal of Multimedia and Ubiquitous Engineering, 2016
This paper resolves the problem of channel estimation and identification of a nonminimum phase system using three algorithms. These algorithms play an important role for estimating blindly the parameters of radio mobile channel. Thus studying the blind's problem channel equalization based on the following, Proposed Algorithm, CMA and SKMAA equalizers. The simulation results in the noisy environment and for different SNR values demonstrate that Proposed Algorithm is more performing than the other algorithms. In addition, the Sign Kurtosis Maximization Adaptive Algorithm (SKMAA) is more powerful compared to Constant Modulus Algorithm (CMA), that is to say it gives the right blind channel equalization.
Error correcting least-squares Subspace algorithm for blind identification and equalization
Signal Processing, 2001
A subspace based blind channel identiÿcation algorithm using only the fact that the received signal can be oversampled is proposed. No direct use is made in this algorithm of either the statistics of the input sequence or even of the fact that the symbols are from a ÿnite set and therefore this algorithm can be used to identify even channels in which arbitrary symbols are sent. Using this algorithm as a base and using the extra information which becomes available when the transmitted symbols are from a known ÿnite set, the EC-LS-Subspace algorithm is derived. The EC-LS-Subspace algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use the statistical information contained in the received signal directly. In the noiseless case, if some conditions are met, it is possible for the proposed Basic Subspace algorithm to identify the channel exactly using an observation interval of just (J +2)T , if the length of the impulse response of a channel is JT; T being the symbol interval. In the noisy case, simulations have shown that the channel can be identiÿed accurately by using a very small observation interval (comparable to (J + 2)T).
Adaptive multi-channel least mean square and Newton algorithms for blind channel identification
Signal Processing, 2002
The problem of identifying a single-input multiple-output FIR system without a training signal, the so-called blind system identiÿcation, is addressed and two multi-channel adaptive approaches, least mean square and Newton algorithms, are proposed. In contrast to the existing batch blind channel identiÿcation schemes, the proposed algorithms construct an error signal based on the cross relations between di erent channels in a novel, systematic way. The corresponding cost (error) function is easy to manipulate and facilitates the use of adaptive ÿltering methods for an e cient blind channel identiÿcation scheme. It is theoretically shown and empirically demonstrated by numerical studies that the proposed algorithms converge in the mean to the desired channel impulse responses for an identiÿable system.
A subspace based blind identification and equalization algorithm
A subspace based blind channel identification algorithm is proposed here. This algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use t h e statistical information contained in t h e received signal directly. In the noiseless case, this algorithm uses t h e least number of symbols that can possibly be used t o identify the channel exactly. In t h e noisy case, simulations have shown that almost exact identification can be obtained by using a few more symbols than the theoretical minimum. This is orders of magnitude better than the other blind algorithms. Once t h e channel has been identified by using this procedure, any of the existing equalization techniques can be used along with it to obtain the symbols.
A NEW METHOD FOR DECISION-DIRECTED BLIND EQUALIZATION ALGORITHM
The decision-directed blind equalization algorithm is often used due to its simplicity and good convergence property when the eye pattern is open. However, in a channel where the eye pattern is closed, the decision-directed algorithm is not guaranteed to converge. Hence, a modified decision-directed algorithm using a hyperbolic tangent function for zero-memory nonlinear function has been proposed and applied to avoid this problem by Filho et al. But application of this algorithm includes the calculation of hyperbolic tangent function and its derivative or a look-up table which may need a large amount of memory due to channel variations. To reduce the computational and/or hardware complexity of Filho's algorithm, in this paper, a linearization method of the zero-memory nonlinear function is proposed. It is shown that the proposed scheme, as it is combined with decision-directed algorithm, not only reduces the computational complexity but also enhances the convergence and steady-state performance of the adaptive algorithm.
Further results on blind identification and equalization of multiple FIR channels
1995 International Conference on Acoustics, Speech, and Signal Processing, 1995
In previous work, we have shown that in the case of multiple antennas and/or oversampling, FIR ZF equalizers exist for FIR channels and can be obtained from the noise-free linear prediction (LP) problem. The LP problem also lead to a minimal parameterization of the noise subspace, which was used to solve the deterministic maximum likelihood (DML) channel estimation problem. Here we present further contributions along two lines. One is a number of blind equalization techniques of the adaptive ltering type. We also present some robustifying modi cations of the DML problem.
9Th European Signal Processing Conference, 1998
The least-squares and the subspace methods are two well-known approaches for blind channel identification/ equalization. When the order of the channel is known, the algorithms are able to identify the channel, under the so-called length and zero conditions. Furthermore, in the noiseless case, the channel can be perfectly equalized. Less is known about the performance of these algorithms in the practically inevitable cases in which the channel possesses long tails of "small" impulse response terms. We study the performance of the m m mth-order least-squares and subspace methods using a perturbation analysis approach. We partition the true impulse response into the m m mth-order significant part and the tails. We show that the m m mth-order least-squares or subspace methods estimate an impulse response that is "close" to the m m mth-order significant part. The closeness depends on the diversity of the m m mth-order significant part and the size of the tails. Furthermore, we show that if we try to model not only the "large" terms but also some "small" ones, then the quality of our estimate may degrade dramatically; thus, we should avoid modeling "small" terms. Finally, we present simulations using measured microwave radio channels, highlighting potential advantages and shortcomings of the least-squares and subspace methods.
PERFORMANCE COMPARISONS OF ADAPTIVE ALGORITHMS FOR BLIND EQUALIZATION
In this paper, we compare several algorithms for blind channel equalization. The analysis includes the joint order detection and blind channel estimation by least squares smoothing (J-LSS), the adaptive version of the J-LSS algorithm, and the prediction error algorithm. Analysis are performed with respect to the computa-tional complexity and convergence speeds of the algo-rithms.