Error Propagation Phenomenon Research Articles

Joint Data Detection and Channel Tracking for OFDM Systems With Phase Noise

This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.

Performance analysis of the weighted decision feedback equalizer

In this paper, we analyze the behavior of the weighted decision feedback equalizer (WDFE), mainly from filtering properties aspects. This equalizer offers the advantage of limiting the error propagation phenomenon. It is well known that this problem is the main drawback of decision feedback equalizers (DFEs), and due to this drawback DFEs are not used very often in practice in severe channels (like wireless channels). The WDFE uses a device that computes a reliability value for making the right decision and decreasing the error propagation phenomenon. We illustrate the WDFE convergence through its error function. Moreover regarding the filtering analysis, we propose a Markov model of the error process involved in the WDFE. We also propose a way to reduce the number of states of the model. Our model associated with the reduction method permits to obtain several characteristic parameters such as, error propagation probability (appropriate to qualify the error propagation phenomenon), time recovery and error burst distribution. Since the classical DFE is a particular case of the WDFE (where the reliability is always equal to one); our model can be applied directly to DFE. As a result of the analysis of this process, we show that the error propagation probability of the WDFE is less than that of the classical DFE. Consequently, the length of the burst of errors also decreases with this new WDFE. Our filtering model shows the efficiency of the WDFE.

A New Flexible Approach to Estimate the IA and IF of Nonstationary Signals of Long-Time Duration

In this paper, we propose an original strategy for estimating and reconstructing monocomponent signals having a high nonstationarity and long-time duration. We locally apply to short-time duration intervals the strategy developed in our previous work about nonstationary short-time signals. This paper describes a nonsequential time segmentation that provides segments whose lengths are suitable for modeling both the instantaneous amplitude and frequency locally with low-order polynomials. Parameter estimation is done independently for each segment by maximizing the likelihood function by means of the simulated annealing technique. The signal is then reconstructed by merging the estimated segments. The strategy proposed is sufficiently flexible for estimating a large variety of nonstationarity and specifically applicable to high-order polynomial phase signals. The estimation of a high-order model is not necessary. The error propagation phenomenon occurring with the known approach, the higher ambiguity function (HAF)-based method, is avoided. The proposed strategy is evaluated using Monte Carlo noise simulations and compared with the Cramer-Rao bounds (CRBs). The signal of a songbird is used as a real example of its applicability.

Fast Computation of Constrained Decision Feedback Equalizers

Constrained formulations of decision feedback equalizer (DFE) schemes arise whenever its intrinsic error propagation phenomenon must be reduced. This is the case when linear or a quadratic norm limits are enforced in the formulation of the usual minimum mean-square-error (mmse) DFE problem, so that superior performance can be achieved when compared to its unconstrained version. This paper solves the problem of designing fast algorithms for computing the DFE filters under the so-called magnitude and energy limiting norm criteria. The former is obtained by developing new updates in addition to the ones efficiently computed via a fast Kalman based method recently introduced. In a constrained energy formulation, however, it turns out that the desired shift structure of the channel convolution matrix that allows for a fast Kalman algorithm no longer exists. Still, we shall show how to properly correct for this discrepancy in structure in order to provide a fast recursion for computing the DFE coefficients in the constrained energy scenario. We verify the accuracy of the new algorithms under finite precision via computer simulations

Error resilient GOP structures on video streaming

In this paper, we describe novel coding dependencies among video frames supporting efficient error resilience in a video streaming system. Our approach is based upon reorganizing the regular linear GOP encoding structure to prevent the error propagation phenomenon and recover lost frames by interpolation. For errors taking place in a single frame, we guarantee that errors will not propagate to the neighboring frames. Therefore, both the past and future temporal information can be used to reconstruct the damaged frame. Furthermore, the proposed coding structures can also recover successive lost frames. In low power and low bandwidth circumstances such as some mobile devices, the proposed structures can also dynamically adjust the video quality level to adapt to a fluctuant network environment.

Error-resilient first-order multiplexed source codes: performance bounds, design and decoding algorithms

This paper describes a new family of error-resilient variable-length source codes (VLCs). The codes introduced can be regarded as generalizations of the multiplexed codes described by Je/spl acute/gou and Guillemot. They allow to exploit first-order source statistics while, at the same time, being resilient to transmission errors. The design principle consists of creating a codebook of fixed-length codewords (FLCs) for high-priority information and in using the inherent codebook redundancy to describe low-priority information. The FLC codebook is partitioned into equivalence classes according to the conditional probabilities of the high-priority source. The error propagation phenomenon, which may result from the memory of the source coder, is controlled by choosing appropriate codebook partitions and index assignment strategies. In particular, a context-dependent index assignment strategy, called crossed-index assignment, is described. For the symbol error rate criterion, the approach turns out to maximize the cardinality of a set of codewords, called the code kernel, offering synchronization properties. The decoder resynchronization capability is shown to be further increased by periodic use of memoryless multiplexed codes. Theoretical and practical performances in terms of compression efficiency and error resilience are analyzed.

Iterative Design and Detection of a DFE in the Frequency Domain

Error-propagation phenomena and computational complexity of the filters' design are important drawbacks of existing decision-feedback equalizers (DFE) for dispersive channels. In this paper, we propose a new iterative block DFE (IBDFE) which operates iteratively on blocks of the received signal. Indeed, a suitable data-transmission format must be used to allow an efficient implementation of the equalizer in the frequency domain, by means of the discrete Fourier transform. Two design methods are considered. In the first method, hard detected data are used as input of the feedback, and filters are designed according to the correlation between detected and transmitted data. In the second method, the feedback signal is directly designed from soft detection of the equalized signal at the previous iteration. Estimators of the parameters involved in the IBDFE design are also derived. From performance simulations on a wireless dispersive fading channel, we observed that the IBDFE outperforms existing DFEs. Moreover, the IBDFE exhibits a reduction of the computational complexity when compared against existing schemes, both in signal processing and in filter design.

Soft-threshold-based multilayer decision feedback equalizer (STM-DFE) algorithm and VLSI architecture

The decision feedback equalizer (DFE) is an efficient scheme to suppress intersymbol interference (ISI) in various communication and magnetic recording systems. However, most cost-effective DFE implementations suffer from the phenomenon of error propagation, which degrades its bit error rate (BER) performance. This paper proposes a soft-threshold-based multilayer DFE (STM-DFE) technique to reduce the BER. It involves very low hardware overhead costs as compared to the conventional DFE. When applied to a practical Lorentzian channel and channels of different eigenvalue spread, the STM algorithm even outperforms the Ideal DFE (IDFE) system (in the IDFE, symbols are correctly fed back without propagation errors). Simulation results show that the proposed scheme can efficiently reduce the burst error length (BEL) as well as BER. Additionally, the hardware overhead to implement the STM-DFE algorithm is negligible compared with the conventional DFE. By using the concepts of Shanbhag and Parhi and of Yang, Wu, and Lai, we also propose the pipelined STM-DFE (PSTM-DFE) architecture for high-speed communication/storage applications.

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.

Techniques for minimizing error propagation in decision feedback detectors for recording channels

Decision feedback equalization (DFE) is a low-cost approach for signal detection in magnetic recording channels. However it suffers from the phenomenon of error propagation (EP). In this paper two approaches are proposed for minimizing EP in DFE. The first approach imposes appropriate constraints on the feedback equalizer. The second approach uses simple threshold techniques to detect the presence of decision errors and to either correct these errors or minimize the possibility of further errors. Computer simulations show that the proposed techniques reduce EP very significantly. Theoretical analyses of the threshold techniques are presented to quantify the associated false and correct detection probabilities. By combining the analysis with an existing EP simulation method, a fast simulation procedure is derived for estimating EP and bit error rate (BER).

Statistical analysis of the product high-order ambiguity function

The high-order ambiguity function (HAF) was introduced for the estimation of polynomial-phase signals (PPS) embedded in noise. Since the HAF is a nonlinear operator, it suffers from noise-masking effects and from the appearance of undesired cross terms and, possibly, spurious harmonics in the presence of multicomponent (mc) signals. The product HAF (PHAF) was then proposed as a way to improve the performance of the HAF in the presence of noise and to solve the ambiguity problem. In this correspondence we derive a statistical analysis of the PHAF in the presence of additive white Gaussian noise (AWGN) valid for high signal-to-noise ratio (SNR) and a finite number of data samples. The analysis is carried out in detail for single-component PPS but the multicomponent case is also discussed. Error propagation phenomena implicit in the recursive structure of the PHAF-based estimator are explicitly taken into account. The analysis is validated by simulation results for both single- and multicomponent PPSs.

Joint equalization and coding for intersymbol interference channels

We present a novel scheme that combines decision feedback equalization (DFE) with high-rate error-detection coding in an efficient manner. The proposed scheme is shown to considerably outperform the conventional practice on channels with high SNR, such as those encountered in twisted-pair telephony systems. In order to analyze the performance of our method, we introduce an approximate mathematical model taking into account the error propagation phenomenon. Based on this model, upper and lower bounds on the overall probability of error are developed. These show that a simple low-redundancy error-detecting code, when properly integrated with the equalizer, can make the overall probability of error several orders of magnitude lower than that obtained with the conventional DFE, or with a DFE followed by an error-correcting code. Computer simulations of the proposed method have been performed for several channels, including the so-called high-bit-rate digital subscriber line (HDSL) test-loop 4, which is known to have a considerable amount of intersymbol interference. For all these channels, our results show that a reduction in the probability of error by more than three orders of magnitude can be obtained using codes of rate 0.96 and above. This, in turn, translates into power savings (coding gain) of 2.5 to 3 dB.

Error analysis: A tool for selecting travel demand models

In developing travel demand models it is generally assumed that the base-year data used in developing the parameters, as well as the forecasted data to be used as independent variables for the design year, are of acceptable quality. The purpose of this paper is to present the application of one type of travel demand forecasting model (multinomial logit models) and to demonstrate how error considerations can be used as a tool for identifying the optimal model. Further research is recommended to develop better insights into the phenomenon of error propagation so that the consideration of errors can be a factor in decisions on model selection.

Error analysis: a tool for selecting travel demand models

In developing travel demand models it is generally assumed that the base-year data used in developing the parameters, as well as the forecasted data to be used as independent variables for the design year, are of acceptable quality. The purpose of this paper is to present the application of error propagation theory in assesing the predictive quality of one type of travel demand forecasting model (multinomial logit models) and to demonstrate how error considerations can be used as a tool for identifying the optimal model. The general conclusions of this study are that: (1) it is indeed possible to quantify errors in dependent variables in logit models as a consequence of errors in independent variables; and (2) error consideration can be used as a tool for identifying the optimal model from a set of candidate models. Further research is recommended to develop better insights into the phenomenon of error propagation so that the consideration of errors can be a factor in decisions on model selection.