Equalization Of Communication Channels Research Articles

Integrated Silicon Photonic Reservoir Computing With PSO Training Algorithm for Fiber Communication Channel Equalization

Integrated Silicon Photonic Reservoir Computing With PSO Training Algorithm for Fiber Communication Channel Equalization

Non-linear Channel Equalization using Modified Grasshopper Optimization Algorithm

In this paper, a modified grasshopper optimization algorithm is proposed for equalization of non-linear wireless communication channels. Even though grasshopper optimisation algorithm (GOA) is an efficient algorithm, it gets trapped into local optima after some iterations due to loss of swarm diversity. Furthermore, GOA does not involve any provision to retain the elite grasshoppers found so far at each index which weakens the exploitation capability and convergence rate of GOA. These limitations of GOA are alleviated in this paper by incorporating three key modifications into GOA. A threshold parameter is introduced to detect the inefficient search region. Lévy Flight is integrated with the basic GOA to improve the diversity of grasshopper swarm and the greedy selection operator is used to preserve the elite grasshoppers found so far at every index. The superiority of a modified grasshopper optimization algorithm (MGOA) is illustrated over the existing metaheuristic algorithms. The key parameters of MGOA are selected by performing the sensitivity analysis. The simulation results on four non-linear channels demonstrate the equalization capability of the proposed MGOA in terms of MSE and BER performance. A statistical validity of results provided by MGOA is confirmed through Wilcoxon rank-sum test.

Relationship Between Criteria Based on Correntropy and Second Order Statistics for Equalization of Communication Channels

Relationship Between Criteria Based on Correntropy and Second Order Statistics for Equalization of Communication Channels

Using Least Squares Support Vector Machines for Adaptive Communication Channel Equalization

Adaptive equalizers are used in digital communication system receivers to mitigate the effects of non-ideal channel characteristics and to obtain reliable data transmission. In this paper, we adopt least squares support vector machines (LS-SVM) for adaptive communication channel equalization. The LS-SVM involves equality instead of inequality constraints and works with a least squares cost function. Since the complexity and computational time of a LS-SVM equalizer are less than an optimal equalizer, the LS-SVM equalizer is suitable for adaptive digital communication and signal processing applications. Computer simulation results show that the bit error rate of the LS-SVM equalizer is very close to that of the optimal equalizer and better than multilayer perceptron (MLP) and wavelet neural network (WNN) equalizers.

Using Communication Channel Equalization to Remove Atmospheric Turbulence in Star Signal Detection

In this paper, we explore the problem of removing atmospheric turbulence to obtain better images of remote sidereal star systems. The imaging process is remodeled as a transmit–receive wireless communication paradigm in a novel approach for correcting space- and time-varying blur in stellar images. In particular, the effect of starlight passing through atmospheric turbulence is modeled as multipath fading communication channels. The problem is then transformed into channel equalization in space and time domains to produce a sharp stellar image. This approach involves first estimating the center of a blurred stellar image. Next, linear regression obtains an equivalent two-dimensional channel response through decomposition of the blurred image into the weighted sum of the diffraction-limited patterns in the spatial domain. Finally, an alignment algorithm is implemented for synthesis, and a final output is generated. Experiments were performed with real field-captured images; the results revealed that this approach could be used to effectively correct image blur and obtain diffraction-limited stellar images.

LMS Algorithm for Adaptive Transversal Equalization of a Linear Dispersive Communication Channel

The presence of any type of distortion in communication system, regardless of the causes, is undesirable and undeniably has a negative impacts on the system in general and therefore it is necessary to eliminate its effects. This study employs one of the well-known algorithms for adaptive equalization of linear dispersive communication channel which is Least Mean Square (LMS) algorithm. The LMS technique is basically utilized to eliminate the noise in communication channel. The novelty of this paper includes the profoundly analyzing of the influence of rate of convergence, miss-adjustment, computational requirement, and sensitivity to Eigen-value spread in sufficient details in a simple and plain way. Moreover, the system performance improvement employing the feedback equalizer technique is intensively presented which shows that our methodology is very effective to eliminate the noise in the system. The simulation work has been performed with MATLAB software.

Channel Equalization Based on Decision Trees

This paper analyzes the application of decision trees to the problem of communication channel equalization. Decision trees are interesting structures because they are nonlinear and relatively simple from a computational standpoint. They are tested for channel models that give rise to classification tasks of different complexity and compared to the Bayesian equalizer and the Wiener linear equalizer. The results are quite encouraging, as they show that the tree-based equalizer reaches, in many cases, a performance similar to that of the Bayesian filter at a lower computational cost.

Equalization of Frequency Selective Channels Using Multirate Signal Processing

Multirate signal processing techniques for the equalization of frequency selective communications channels are proposed. It is shown that a multirate equalizer (MRE) is substantially less complex compared to the conventional channel equalizers. This feature makes single-carrier transmission feasible in channels having much longer delay-spreads or wider bandwidths than it was possible earlier. The proposed algorithms do not require transmission of a cyclic prefix. They have both linear and nonlinear versions, wherein the latter employs successive interference cancellation similar to the decision-feedback equalizer (DFE). It is possible to perform decoding prior to interference cancellation. Thus, successive interference canceling (SIC) MRE does not suffer from error propagation if operating on an achievable rate. The SIC-MRE can provably achieve the independent identically distributed information rate of the channel if a Gaussian input alphabet is used. For finite input alphabets, numerical results demonstrate that the single-carrier transmission with the SIC-MRE outperforms multicarrier transmission in terms of throughput. This result holds not only for low-order modulations and relatively narrow bandwidths but also for high-order modulations and wide bandwidths.

Efficient System Tracking With Decomposable Graph-Structured Inputs and Application to Adaptive Equalization With Cyclostationary Inputs

This paper introduces the graph-structured recursive least squares (GS-RLS) algorithm, which is a very efficient means to track a linear time-varying system when the inputs to the system have structure that can be modeled using a decomposable Gaussian graphical model. For graphs with small clique sizes, it is shown that GS-RLS can achieve tracking performance very close to that of the conventional RLS algorithm for a fraction of the computational cost. In particular, after proving that the outputs of wide-sense stationary time-varying communication channels have graphical model structure if the inputs are cyclostationary, significant computational gains are realized for adaptive equalization of the time-varying underwater acoustic communication channel using the GS-RLS algorithm. This is verified using field data from the SPACE08 underwater acoustic communication experiment.

Environmental Model-Based Time-Reversal Underwater Communications

This paper addresses channel compensation in underwater acoustic communications by proposing a method for inserting physical propagation modeling into a passive time-reversal (PTR) receiver. PTR is known as a low complexity channel equalizer that uses multichannel probing for time signal refocusing, reducing inter-symbol interference caused by multipath propagation. The proposed method aims to improve PTR communications performance by replacing the conventional noisy channel estimates with optimized and noiseless channel replicas computed by a numerical ray trace model. The optimization consists of environmental focalization in an “ a priori ” physical parameter search space to obtain “ a posteriori ” channel impulse response replicas that best match the observed data. The results obtained on two data sets acquired during the UAN’11 experiment in a shallow water fjord near Trondheim, in May 2011, show that the proposed method clearly outperformed the traditional PTR by a mean square error gain from 1 up to 4 dB. Channel tracking was effective despite a reduced physical parameter search space that could be exhaustively covered with a minimal computational effort. To the best of our knowledge, this is the first successful report on the usage of a physical parameter fed numerical model for underwater acoustic communications channel equalization with real transmitted data in a useful underwater modem frequency band.

PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm

Wireless communication always suffers from intersymbol interference (ISI), multipath fading, co-channel interference (CCI). Equalization techniques try to combat these phenomena to recover distorted data at receivers. Most techniques need a separate process to estimate channel profile before equalization. In this paper, Particle Swarm Optimization tuned Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) based channel equalizer, which is capable of system identification, estimation and equalization of wireless communication channels, is proposed. PSO-ANFIS equalizer uses the training data and employs fuzzy C-means (FCM) clustering to model a wireless communication channel without knowledge of channel dynamics. The PSO-ANFIS equalizer was simulated on a mobile communication model with ISI, CCI and the Additive White Gaussian Noise. The used training method and FCM provided the best regression of system modeling to fit to wireless channel. The performances of PSO-ANFIS equalizer were evaluated and compared to the equalizers of Maximum-Likelihood Sequence Estimation, Recursive Least Squares, ANFIS, Functional Link Artificial Neural Network, PSO-Variable Constriction Factor and Artificial Neural Network-PSO in terms of BER–SNR. The simulation results showed that the performance of the PSO-ANFIS equalizer with FCM clustering gives the best performance among the mentioned nonlinear equalization techniques.

Neural Networks Based Equalizer for Signal Restoration in Digital Communication Channels

One of the main obstacles to reliable communications is the inter symbol interference (ISI). An equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and restore the transmitted signal. This paper presents the equalization of digital communication channels using artificial neural network structures. The performances of a nonlinear equalizer using multilayer perceptron (MLP) trained by the back propagation algorithm is compared with a conventional linear traversal equalizer (LTE). Simulation results show that the performances of the MLP based Equalizer surpass significantly the classical LTE in term of the restored signal, the steady state mean square error (MSE) achievable and the minimum bit error rate attainable. The consistency in performance is observed in minimum phase and non-minimum phase channels as well.

Neural Networks Based Equalizer for Signal Restoration in Digital Communication Channels

One of the main obstacles to reliable communications is the inter symbol interference (ISI). An equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and restore the transmitted signal. This paper presents the equalization of digital communication channels using artificial neural network structures. The performances of a nonlinear equalizer using multilayer perceptron (MLP) trained by the back propagation algorithm is compared with a conventional linear traversal equalizer (LTE). Simulation results show that the performances of the MLP based Equalizer surpass significantly the classical LTE in term of the restored signal, the steady state mean square error (MSE) achievable and the minimum bit error rate attainable. The consistency in performance is observed in minimum phase and non-minimum phase channels as well.

Neural Networks Based Equalizer for Signal Restoration in Digital Communication Channels

One of the main obstacles to reliable communications is the inter symbol interference (ISI). An equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and restore the transmitted signal. This paper presents the equalization of digital communication channels using artificial neural network structures. The performances of a nonlinear equalizer using multilayer perceptron (MLP) trained by the back propagation algorithm is compared with a conventional linear traversal equalizer (LTE). Simulation results show that the performances of the MLP based Equalizer surpass significantly the classical LTE in term of the restored signal, the steady state mean square error (MSE) achievable and the minimum bit error rate attainable. The consistency in performance is observed in minimum phase and non-minimum phase channels as well.

Dynamic Characterization and Equalization of a Power Line Communication Channel

Power Line Communication (PLC) systems need signal processing algorithms in order to recover the transmitted signals from the high degrading medium. In the literature exist some techniques that work on the basis of a constant, known channel, but it was demonstrated that the PLC channel is time variant, therefore these techniques lose efficiency. This work presents a dynamic equalization algorithm computed in the time domain, where the channel characterization is done by a recursive least squares algorithm using the transmitted data as reference. The proposal was tested with simulations and was statistically validated using in time variant PLC channel models, obtaining a lower error rate than other methods.

Blind channel‐shortening for multipath channels using a recursive prediction‐error filter with additional prediction delay

The prediction-error filter is a well-known tool for blind communication channel equalisation. For multipath channels, it has two disadvantages considered here. First, in transversal form, it must be substantially longer than the impulse response of the channel, with a consequent high computation cost; second, it cannot fully equalise mixed-phase channels. Here, the authors examine a recursive version of the linear predictor, used with additional prediction delay for blind channel-shortening of multipath channels with multi-carrier modulation signals using a cyclic prefix. The recursive mode of operation allows the filter length to be significantly shorter than in the transversal, non-recursive form. In tests using modelled channels, they observe two significant results. First, for channels that include maximum-phase terms, the additional prediction delay ameliorates the filter response to maximum-phase terms. Thus, channel-shortening may be achieved, at least partially, unless the maximum-phase terms are severe. Second, the recursive prediction-error filter impulse response adapts to a stable minimum-phase form. This low-cost technique, then, may be used to augment the use of a cyclic prefix in reducing inter-symbol interference in multipath channels.

Multi-dimensional complex-valued Gabor wavelet networks

This paper proposes a new complex-valued wavelet network based on Gabor transfer functions which have complex-valued weights, input/output variables and translation parameters. A learning algorithm is developed by using the technique of genetic algorithm to facilitate implementation of the network. The effectiveness of the proposed network is evaluated by applying the network to approximate several complicated nonlinear functions, and to tackle the 4-QAM (quadrature amplitude modulation) digital communications channel equalization and the image enhancement via feature extraction problems in signal processing. Indeed, the evaluation results obtained by testing the proposed wavelet network with real facial and textile fabric images clearly show that it is a very useful tool for solving problems in the field of complex-valued signal analysis.

Receiver-channel based adaptive blind equalization approach for GPS dynamic multipath mitigation

Aiming at mitigating multipath effect in dynamic global positioning system (GPS) satellite navigation applications, an approach based on channel blind equalization and real-time recursive least square (RLS) algorithm is proposed, which is an application of the wireless communication channel equalization theory to GPS receiver tracking loops. The blind equalization mechanism builds upon the detection of the correlation distortion due to multipath channels; therefore an increase in the number of correlator channels is required compared with conventional GPS receivers. An adaptive estimator based on the real-time RLS algorithm is designed for dynamic estimation of multipath channel response. Then, the code and carrier phase receiver tracking errors are compensated by removing the estimated multipath components from the correlators’ outputs. To demonstrate the capabilities of the proposed approach, this technique is integrated into a GPS software receiver connected to a navigation satellite signal simulator, thus simulations under controlled dynamic multipath scenarios can be carried out. Simulation results show that in a dynamic and fairly severe multipath environment, the proposed approach achieves simultaneously instantaneous accurate multipath channel estimation and significant multipath tracking errors reduction in both code delay and carrier phase.

Investigation of Phase Noise on the Performance of LMS-RLS Adaptive Equalizer

This paper investigates the effect of phase noise on equalization of communication channels using least mean square (LMS) and recursive least square (RLS) adaptive algorithms. The aim of the investigation is to mitigate inter-symbol interference (ISI) caused by the channel and to impose the bit error rate (BER) in the received signals. The equalizerusestwobasicadaptivealgorithms: LMS algorithmand RLS algorithm. Without LMS-RLS equalizer,theBER ismorethan when the system modelincludesLMS-RLS equalizer as indicated in table (1) and table (2). Equalizer algorithm is analyzed using MATLAB v.9 Communication Block Set.

Channel equalization using quasi type-2 fuzzy strategies

This paper presents a proposal for channel equalization of non-linear time varying communication channels that uses strategies based on quasi type-2 fuzzy sets. Both triangular and trapezoidal quasi type-2 fuzzy equalizers are designed by means of an incremental design approach. A comparative experimental study among several fuzzy equalization architectures is carried out, showing that the equalizer based on trapezoidal quasi type-2 fuzzy sets exhibits the best performance for low and medium levels of uncertainty. The results of this work also suggest that high order fuzzy sets make the equalization process more robust to the variability of the communications channel.