Blind Decision Feedback Equalizer Research Articles

Modification in the Range of Information Potentials for Blind Algorithms in Impulsive Noise Environments

In this paper a modification approach to the range of influence for information potentials (IP)of blind decision feedback equalizer (DFE) algorithms is presented to cope better with impulsive noise and severe channel distortion. Analysis of the IP range of influence reveals that the range should be applied separately based on the roles under impulsive noise. A new cost function that can manage ranges of information potentials is proposed, and a related DFE algorithm is derived. Simulation results from the environment of a severe multipath channel obtained from shallow-water communication experiments and impulsive noise show that the proposed DFE algorithm yields more than 10 dB in MSE gain by employing the proposed modification toranges of information potentials.

A blind decision feedback equalizer with efficient structure-criterion switching control

This paper considers and proposes an innovated method of structure criterion switching control for the self-optimized blind decision feedback equalizer (DFE) scheme which operates by switching between adaptation modes according to the mean square error (MSE) convergence state. The new switching control shortens the blind acquisition period time of the DFE and, consequently, speeds up its effective convergence rate. The switching control is based on the variable switching threshold which combines the commonly used MSE estimate of the DFE?s output and a posteriori error of the all-pole whitener performing front-end amplitude equalization during the blind operation mode. The efficiency of the DFE switching control is verified by simulations of single-carrier system transmitting QAM signals over multipath channels.

Blind adaptive correlation-based decision feedback equalizer (DFE) for underwater acoustic communications

Passive time reversal uses a set of matched filters corresponding to individual channel impulse response to reduce intersymbol interferences (ISIs) in underwater acoustic communications. Because of residual ISIs after time reversal, a coupled single decision feedback equalizer (DFE) is necessary. The coupled DFE uses a fixed tap number applicable to most shallow oceans with less user supervision. However, this correlation-based DFE needs the training sequence to estimate the individual channel response and initialize DFE, which decreases the effective bit rate and increases user supervision. We therefore in this paper propose a new blind structure of time reversal coupling with DFE, and it can complete multichannel identification and equalization at the same time without the training sequence. This new receiver has a reversible structure. In the first mode, it is linear and adapted by blind algorithms, e.g. constant modulus algorithm (CMA), to estimate the multichannel impulse responses and initialize the equalizer. In the second mode, the receiver becomes correlation-based DFE. From the viewpoint of user supervision and spectral efficiency, the new structure with no training sequence is more attractive. The blind correlation-based DFE exhibits the same steady-state mean square error (MSE) with the trained structure, and has been validated on real underwater communication data.

Maximum entropy whitening algorithm with adaptive neuron slope for blind DFE

The addressed blind decision feedback equalizer (DFE) reverses the classical order of its feed-forward and feedback filters at the beginning of adaptation to achieve the best equalization of the minimum and maximum phase components of a channel transfer function. Although very effective, this blind equalization approach deals with the feedback filter mismatch at the time of its transformation from the front-end all-pole whitener of the received signal to the decision-directed feedback equalizer placed after the feedforward filter. To eliminate this weakness, the adaptive neuron slope is introduced instead of the fixed one into the stochastic gradient whitening algorithm based on the joint entropy maximization cost. The performance of the innovated algorithm is verified by simulating m-QAM (quadrature amplitude modulation) signals transmission over multipath channels. The algorithm with the adaptive neuron slope achieves a better whitening of the received signal spectrum, and, hence, increases the equalization successfulness.

The blind decision feedback equalizer with the entropic: Leaky decorrelation algorithm

This paper presents the blind decision feedback equalizer (DFE) which transforms its soft feedback filter (SFBF) from the linear decor relator of received signal to the nonlinear soft feedback equalizer, and vice reversa, in order to eliminate the error propagation effects. Aiming to increase SFBF robustness in relation to the complexity of 64-QAM signal, the entropic stochastic gradient algorithm is enhanced by coefficient leaky term which restricts the overgrowth of the decorrelator coefficients in the ambient of severe inter symbol interference channels. By introducing coefficient leaky, the undesirable effects of the all-pole linear decorrelation are compensated. The achievements of the received signal decorrelation with the entropic-leaky algorithm are verified by the Monte Carlo simulations.

Decision Feedback Blind Equalizer with Tap-Leaky Whitening for Stable Structure-Criterion Switching

The research presented in this paper improves the structure-criterion switching performance of the blind decision feedback equalizer (DFE) which eliminates error propagation effects by optimizing both the structure and the cost criterion. To conquer the complexity of the 64-QAM (quadrature amplitude modulated) signal constellation, the stochastic entropy-gradient algorithm is additionally regularized by the coefficient leaky term to avoid a coefficients norm overgrowth of the received signal whitener. Effectively, the leak of coefficients is employed to ensure a stable structure-criterion switching of DFE between blind and decision-directed operation modes. The optimization of the resulting whitening algorithm is achieved by means of two free, leaky and entropic, parameters which act in opposition to each other. Both, the influence of the 64-QAM signal on the feedback filter behavior and the parametric optimization of the whitening algorithm are analyzed through simulations.

A Dual-mode Blind Equalization Algorithm for Improving the Channel Equalized Performance

In this paper, we propose a new dual-mode blind equalization algorithm to improve the channel equalization performance. First, a kind of new error function is proposed and a nonlinear function between the step-size factor and the new error function is built to realize the variable step-size constant modulus algorithm (CMA). Then, in order to optimize the channel equalization performance, the variable step-size CMA is combined with decision directed least mean square (DDLMS) algorithm to form the new dual-mode algorithm. The new dual- mode algorithm uses either switching-dual mode or weighted dual-mode to update the coefficient of blind decision feedback equalizer. The simulation results show that in the situation of Gaussian white noise with 30dB signal-to-noise ratio, the proposed algorithm reduces the steady-state error to 12dB and drops inter symbol interference to 31dB in the condition of satellite channel. The new dual-mode algorithm not only increases the rate of convergence but also reduces the steady- state error. The proposed algorithm can be widely used in different communication channels such as the satellite channel.  Index Terms—Blind equalization; constant modulus algorithm; variable step-size; decision directed least mean square.

랜덤 심볼열과 결정 궤환을 사용한 자력 등화 알고리듬

Abstract Non-linear equalization techniques using decision feedback structure are highly demanded for cancellation of intersymbol interferences occurred in severe channel environments. In this paper decision feedback structure is applied to the linear blind equalizer algorithm that is based on information theoretic learning and a randomly generated symbol set. At the decision feedback equalizer (DFE) the random symbols are generated to have the same probability density function (PDF) as that of the transmitted symbols. By minimizing difference between the PDF of blind DFE output and that of randomly generated symbols, the proposed DFE algorithm produces equalized output signal. From the simulation results, the proposed method has shown enhanced convergence and error performance compared to its linear counterpart. Key Words : Decision feedback, Blind equalization, PDF, Random symbols, ITL. * 교신저자 : 김남용(namyong@kangwon.ac.kr)접수일 11년 11월 02일 수정일 (1차 11년 12월 26일, 2차 11년 12월 29일) 게재확정일 12년 01월 05일

An efficient blind decision feedback equalizer

Efficient and fully blind Decision Feedback Equalizer (DFE) remains an open issue, mainly because of the potential errors in the decision loop. Based on the Weighted Decision Feedback Equalizer (WDFE), our previous work aiming at decreasing the error propagation phenomena, we propose a new blind DFE called Blind Weighted Decision Feedback Equalizer. The main idea of the WDFE was to commute between Linear Recursive Equalizer (LRE) and DFE for the error computation at the decision device. In this paper, we extend this idea in order to commute also both the algorithms and the filtering structure. This commutation is performed softly and blindly.

Blind DFE With Maximum-Entropy Feedback

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This letter proposes an entropy-gradient adaptive feedback filter specially derived for the blind decision feedback equalizer with a self-optimized configuration. Using software simulations, the parametric nonlinearity of the feedback filter is aligned through two operation modes—blind acquisition and soft transition—to respond with a maximum entropy signal. As a result, a simple stochastic gradient algorithm is obtained, and it can be easily optimized for an applied signal constellation. </para>

Adaptive Decision Feedback Space–Time Equalization With Generalized Sidelobe Cancellation

In wireless communications, cochannel interference (CCI) and intersymbol interference (ISI) are two main factors that limit system performance. Conventionally, a beamformer is used to reduce CCI, whereas an equalizer is used to compensate for ISI. These two devices can be combined into one as space-time equalizer (STE). A training sequence is usually required to train the STE prior to its use. In some applications, however, spatial information corresponding to a desired user is available, but the training sequence is not. In this paper, we propose an adaptive decision feedback STE to cope with this problem. Our scheme consists of an adaptive decision feedback generalized sidelobe canceller (DFGSC), a blind decision feedback equalizer (DFE), and a channel estimator. Due to the feedback operation, the proposed DFGSC is not only superior to the conventional generalized sidelobe canceller but also robust to multipath channel propagation and spatial signature error. Theoretical results are derived for optimum solutions, convergence behavior, and robustness properties. With the special channel-aided architecture, the proposed blind DFE can reduce the error propagation effect and be more stable than the conventional blind DFE. Simulation results show that the proposed STE is effective in mitigating both CCI and ISI, even in severe channel environments.

Computationally efficient methods for blind decision feedback equalization of QAM signals

This paper investigates computationally efficient methods for blind decision feedback equalization (DFE) that reduce the complexity and power requirements of blind equalization algorithms while maintaining their steady-state characteristics for quadrature amplitude modulation (QAM) signals. These include the power-of-two error (POT), selective coefficient update (SCU), and frequency-domain block (FDB) methods. A novel radius-directed stop-and-go (RSG) method is introduced, which selectively adjusts the equalizer tap coefficients based on the equalizer output radius. In addition, a new activation/de-activation method based on the equalizer output radius is utilized to control the feedback equalizer (FBE) of the DFEs. Simulation studies and analysis are provided for empirically derived cable and microwave channels and Ricean fading channels.

Near-Optimum Blind Decision Feedback Equalization for ATSC Digital Television Receivers

This paper presents a near-optimum blind decision feedback equalizer (DFE) for the receivers of Advanced Television Systems Committee (ATSC) digital television. By adopting a modified trellis decoder (MTD) with a trace- back depth of 1 for the decision device in the DFE, we obtain a hardware-efficient, blind DFE approaching the performance of an optimum DFE which has no error propagation. In the MTD, the absolute distance is used rather than the squared Euclidean distance for the computation of the branch metrics. This results in a reduction of the computational complexity over the original trellis decoding scheme. Compared to the conventional slicer, the MTD shows an outstanding performance improvement in decision error probability and is comparable to the original trellis decoder using the Euclidean distance. Reducing error propagation by use of the MTD in the DFE leads to the improvement of convergence performance in terms of convergence speed and residual error. Simulation results show that the proposed blind DFE performs much better than the blind DFE with the slicer, and the difference is prominent at the trellis decoder following the blind DFE.

Blind Matched Filter Receiver

Matched filter receiver (MFR) of Forney in the presence of intersymbol interference (ISI) and additive white Gaussian noise is investigated from a blind perspective, which includes blind maximum likelihood sequence detection, blind decision feedback equalization and linear blind equalization. Various realizations of the blind MFR are discussed. In a blind MFR, the channel can be easily estimated using a simple algorithm such as the constant-modulus algorithm (CMA). In this sense, the CMA can be used to estimate the channel directly without complicated matrix decomposition techniques. Zero forcing and minimum mean-square estimation based designs of the blind MFR are explored. Channel estimation using the CMA deteriorates in the presence of noise. A new method to tackle this problem is developed and shown to have satisfactory behavior in low signal-to-noise ratios. Tracking performance of the blind MFR is found to be acceptable in representative Rayleigh fading channels of mobile communication.

Performance analysis of error propagation effects in the dfe for atsc dtv receivers

The paper analyzes the error propagation phenomenon in the decision feedback equalizer (DFE) for the receivers of Advanced Television Systems Committee (ATSC) digital television (DTV) and presents the performance upper-limits of the DFE by comparing various error propagation cases and the no-error propagation case. As one approach to the performance limit, we consider a blind DFE, adopting a trellis decoder with a trace-back depth of 1 as a decision device. Through simulation, we show how much the DFE performance in ATSC DTV receivers is affected by error propagation. We found that while blind equalization is preferable to decision-directed (DD) equalization at signal-to-noise ratio (SNR) values less than 18 dB, DD equalization is superior to blind equalization at SNR values greater than 18 dB. In addition, symbol error rate curves quantitatively show that the performance difference in the DFE caused by error propagation becomes clearer at the trellis decoder following the DFE. The analysis results presented are very informative for developing equalization algorithms for ATSC DTV receivers.

Blind direct DFE estimation using multistep prediction

We propose a blind decision feedback equalizer (DFE) that is characterized by the fact that it does not require channel estimation. Because the output of the optimized multistep prediction error filter (PEF) can be represented as a product of the channel partial impulse response and the transmitted sequence, a backward multistep PEF can be used as the blind DFE feedforward filter. The corresponding feedback filter is obtained from the symbol-rate partial channel impulse response. The proposed algorithm has several advantages over existing blind channel estimation techniques, including stable performance without the necessity of exact channel order estimation.

A fast-acquiring blind predictive DFE

This article presents a blind predictive decision-feedback equalization (DFE) scheme motivated by the work of Labat et al. (see idid., vol.46, p.921-30, July 1998). The proposed scheme outperforms another previously proposed blind predictive DFE scheme, and also eliminates the filter interchange required in the scheme of Labat et al.

Blind decision feedback equalization for VSB-based DTV receivers

We present novel blind decision feedback equalization algorithms for VSB-based DTV receivers by modifying a stop-and-go dual-mode constant modulus algorithm (SAG DMCMA) and applying a selective feedback (SF) scheme to stop-and-go, (SAG) based algorithms. The modification of the SAG DMCMA applies the SAG scheme only to the feedback filter tap update of the DFE, thus reducing the loss of information caused by the SAG policy. The SF scheme, in which we choose the equalizer output or the slicer output for the input of the feedback filter in the DFE according to the SAG flag, diminishes error propagation and thus improves convergence performance. Considering VSB modulation and RF-band effects with the baseband-equivalent VSB channel model in our simulations raises the reliability of our results for practical applications.

Blind decision feedback equalization of time-varying channels with DPSK inputs

This paper presents a new method to the blind equalization of a class of linear time-varying mobile channels for differentially encoded channel input signals. Through reparameterization, the equivalent discrete system can be modeled as a single-input multiple-output system. Unlike some existing methods, this new algorithm does not require prior knowledge on the cyclic frequency of the channel response. The algorithm is simple and relies only on a decision feedback structure and the differential encoding nature of the channel input signals. The convergence analysis of the decision feedback system is presented along with simulation examples.

Blind equalization using parallel Bayesian decision feedback equalizer

The purpose of this paper is to propose a new method for blind equalization using parallel Bayesian decision feedback equalizer (DFE). Blind equalization based on decision-directed algorithm, including the previous proposed Chen’s blind Bayesian DFE, cannot give the correct convergence without the suitable initialization corresponding to the small inter-symbol interference. How to find the suitable initialization becomes the key for the correct convergence. Here, the “start” vector with several states is used to obtain several channel estimates which are the initial channel estimates in proposed method. In these initial channel estimates, the best one which has converged toward the correct result in some degree must exist. The decision-directed algorithm for parallel blind Bayesian DFE is purchased from these initial channel estimates respectively. Evaluating the Bayesian likelihood which is defined as the accumulation of the natural logarithm of the Bayesian decision variable, the correct channel estimates corresponding to the maximum Bayesian likelihood can be found. Compared with Chen’s blind Bayesian DFE, the proposed method presents better convergence performance with less computational complexity. Furthermore, the proposed algorithm works satisfactorily even for channel with severe ISI and in-band spectral null, while Chen’s blind Bayesian DFE fails.