Blind Equalization Algorithm Research Articles

Performance Evaluation of MSAG-SCS-MMA-I Adaptive Blind Equalization Algorithm with dual step-size

Performance Evaluation of MSAG-SCS-MMA-I Adaptive Blind Equalization Algorithm with dual step-size

A Performance Comparison of CM-MMA and RMMA Blind Equalization Algorithm in QAM Signal Transmission

A Performance Comparison of CM-MMA and RMMA Blind Equalization Algorithm in QAM Signal Transmission

Low-complexity blind in-phase/quadrature imbalance compensation and field-programmable gate array realization for coherent receivers

Receiver in-phase/quadrature (I / Q) imbalance caused by imperfect analog receiver front end can rapidly degrade system performance in a coherent optical transmission system. These imbalances, including I / Q gain and phase imbalance, become increasingly important when higher symbol rate, higher modulation order, and low roll-off pulse shape are used. Here, a low-complexity blind equalization algorithm, cascaded phase, and gain imbalance equalizers, is designed to mitigate these interferences. Furthermore, a field-programmable gate array hardware realization with 128-parallel processing units is performed to demonstrate the proposed algorithm in a 32-GBaud 16-quadrature amplitude modulation off-line transmission experiment. Based on the obtained results, the proposed blind I / Q imbalance compensation algorithm can provide very good compensation performance with low logic element consumption, which is more suitable for high order and high baud rate transmission system.

A Robust Maximum Likelihood Algorithm for Blind Equalization of Communication Systems Impaired by Impulsive Noise

To improve the performance of the blind equalizer (BE) in impulsive noise environments, a robust maximum likelihood algorithm (RMLA) is proposed for the communication systems using quadrature amplitude modulation signals. A novel robust maximum likelihood cost function based on the constant modulus algorithm is constructed to effectively suppress the influence of impulsive noise and ensure the computational stability. Theoretical analysis is presented to illustrate the robustness and good computational stability of the proposed algorithm under the impulsive noise ambient. Moreover, it is proved that the weight vector of the proposed BE can converge stably by LaSalle invariance principle. Simulation results are provided to further confirm the robustness and stability of the proposed RMLA.

Blind equalisation in the presence of bounded noise

This study addresses the blind equalisation problem in the presence of bounded noise using an optimal bounding ellipsoid algorithm. This provides an adequate blind equalisation algorithm with an accurate parameter estimation. A fundamental analysis of the involved equaliser is performed to emphasise its underlying properties. This fundamental result is corroborated by promising simulation results.

Blind Equalization Algorithm for Different Eigenvalue Spread Channels

The performance of blind equalization algorithm is closely related to the characteristics of the channel. Eigenvalue spread of the channel can reflect the influence of the channel on the input signal. The paper presents a method to distinguish eigenvalue spread of the wireless channel by the correlation coefficient of the input vector. For channels with different eigenvalue spread, based on the consideration of the complexity and the performance, different blind equalization algorithms are chosen. At the same time a decorrelation blind equalization algorithm is proposed. Simulations verify the effectiveness of the proposed algorithms.

Logarithmic cost-based improved constant modulus type blind channel equalisation algorithm for M-QAM/M-PSK signal

In this paper, authors have proposed the blind channel equalisation algorithm based on logarithmic cost function for QAM/PSK signal which is widely used in the digital communication system. Constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA), the two widely referenced algorithms for blind equalisation of a QAM/PSK signal, exhibit very slow convergence rates and large steady-state mean square error. The proposed algorithm attains faster convergence speed and low steady state mean square error by incorporating the improved logarithmic cost-based error function in the resilient back propagation-based framework. This makes the weight adaption of equaliser simple. Simulation results show that the proposed algorithm has better convergence rate and lower steady state error in comparison to CMA and MCMA algorithms.

Blind Source Separation and Equalization Based on Support Vector Regression for MIMO Systems

In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.

A New Blind Equalization Algorithm for Improving the Channel Equalized Performance

A New Blind Equalization Algorithm for Improving the Channel Equalized Performance

An Equalization Initialization Procedure for MDM Systems Based on Orthogonal Matching Pursuit

In this letter, a new initialization procedure is proposed for channel equalization in the mode division multiplexing (MDM) systems. The initialization procedure is based on the orthogonal matching pursuit (OMP) algorithm. With the proposed method, it is possible to use a short training sequence (TS) to estimate the initial tap values for the constant modulus algorithm (CMA), and the CMA provides further channel equalizer optimization afterward. It is found that with the aid of the OMP algorithm, the length of the TS can be as short as 256 symbols for an MDM system with 35-ns delay spread, which is only 1/16 of the TS length required by the least square algorithm. The OMP based semi-blind algorithm has shorter TS length and a lower computational cost in comparison to the full TS-based channel equalization approaches and a better convergence performance in comparison to the full blind channel equalization algorithms.

Blind I/Q imbalance and nonlinear ISI mitigation in Nyquist-SCM direct detection system with cascaded widely linear and Volterra equalizer

Nyquist-SCM signal after fiber transmission, direct detection (DD), and analog down-conversion suffers from linear ISI, nonlinear ISI, and I/Q imbalance, simultaneously. Theoretical analysis based on widely linear (WL) and Volterra series is given to explain the relationship and interaction of these three interferences. A blind equalization algorithm, cascaded WL and Volterra equalizer, is designed to mitigate these three interferences. Furthermore, the feasibility of the proposed cascaded algorithm is experimentally demonstrated based on a 40-Gbps data rate 16-quadrature amplitude modulation (QAM) virtual single sideband (VSSB) Nyquist-SCM DD system over 100-km standard single mode fiber (SSMF) transmission. In addition, the performances of conventional strictly linear equalizer, WL equalizer, Volterra equalizer, and cascaded WL and Volterra equalizer are experimentally evaluated, respectively.

MIMO blind equalization algorithm based on successive interference cancellation

Modified Constant Modulus Algorithm (MCMA) is widely used in the SISO blind equalization for its simplicity. For MIMO systems MCMA can only equalize one of the source signals. Through the combination of channel estimation and successive interference cancellation, source signals can be equalized in turn. However the recovery of the first source and the channel estimation are the key points, which directly affects the recovery of the subsequent sources. This paper proposes a channel estimation method with a small amount of calculation, and can accurately estimate the channel vector. Meanwhile, a new blind equalization algorithm is put forward to reliably recovery the first source signal. Simulation results verify the effectiveness of the proposed algorithm.

A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

A robust adaptive weighted constant modulus algorithm is proposed for blind equalization of wireless communication systems under impulsive noise environment. The influence of the impulsive noise is analyzed based on numerical analysis method. Then an adaptive weighted constant modulus algorithm is constructed to adaptively suppress impulsive noise. Theoretical analysis is provided to illustrate that the proposed algorithm has a robust equalization performance since the impulsive noise is adaptively suppressed. Moreover, the proposed algorithm has stable and quick convergence due to avoidance of large misadjuntment and adoption of large step size. Simulation results are presented to show the robust equalization performance and the fast convergence speed of the proposed algorithm under both impulsive noise and Gaussian noise environments.

Blind Equalization of Constant Modulus Signals Based on Gaussian Process for Classification

Blind equalization can be combined with soft-input decoders to greatly improve the performance of communication system. However, most blind equalization algorithms are not designed to provide posterior probability, which is essential for soft-input decoders. In this paper, blind equalization based on Gaussian process for classification (GPC), which could output such information, is applied to optimally detect the constant modulus signals. The scheme is implemented by automatically selecting proper initial training data set and continuously incorporating more appropriate points into training data set with a iteration process. During the iteration process, we utilize all equalizer input symbols that can be assumed to be a certain class label at a high probability as training data to make prediction with GPC model, and give out posterior probability of each input symbol under a specific class label. The proposed blind equalizer has been proved to be able to provide a better performance in both linear channel and nonlinear channel compared with other blind equalizers.

Blind equalization and automatic modulation classification based on subspace for subcarrier MPSK optical communications

Equalization can compensate channel distortion caused by channel multipath effects, and effectively improve convergent of modulation constellation diagram in optical wireless system. In this paper, the subspace blind equalization algorithm is used to preprocess M-ary phase shift keying (MPSK) subcarrier modulation signal in receiver. Mountain clustering is adopted to get the clustering centers of MPSK modulation constellation diagram, and the modulation order is automatically identified through the k-nearest neighbor (KNN) classifier. The experiment has been done under four different weather conditions. Experimental results show that the convergent of constellation diagram is improved effectively after using the subspace blind equalization algorithm, which means that the accuracy of modulation recognition is increased. The correct recognition rate of 16PSK can be up to 85% in any kind of weather condition which is mentioned in paper. Meanwhile, the correct recognition rate is the highest in cloudy and the lowest in heavy rain condition.

Blind Equalization Based on Data Reuse and Reliability Labeling

In the absence of a training sequence, blind equalization algorithm usually takes thousands or even tens of thousands symbols to converge, which is especially evident when the channel is severely distorted. When the short data is required to achieve equalization, the current blind equalization algorithms will be difficult to converge. This letter presents a short data reuse blind equalization algorithm based on reliability labeling. If the output symbol corresponding to the same input is labeled twice with reliability during the process of the current and last data reuse, the algorithm will switches to the decision-directed model automatically. At the same time the letter gives a simple way for model switching by a look-up table. Simulations show that the proposed algorithm performs better compared with Modified Constant Modulus Algorithm with long data.

A Blind Identification and Equalization for MC-CDMA Transmission Channel using a New of Adaptive Filter Algorithm

A review of literature shows that there are a variety of adaptive filters. In this research study, we propose a new type of adaptive filter that increases the diversification used to compensate the channel distortion effect in the MC-CDMA transmission. First, we show expressions of the impulse responses of the filter in the case of a perfect channel. The adaptive filter was simulated was experienced by blind equalization for different cases of Gaussian white noise in the case of an MC-CDMA transmission with orthogonal frequency baseband for mobile radio downlink channel Bran A. Simulation Results of the proposed model shows the performance of the identification and blind equalization algorithm for MC-CDMA transmission chain using IFFT.

Enhanced Decision Adjusted Modulus Algorithm for Blind Equalization

A new Enhanced Decision Adjusted Multimodulus Algorithm (EDAMA) for blind equalization is proposed in this article. It is based on ensuring of minimal detection error probability. This algorithm is characterised by smaller steady-state error than Constant Modulus Algorithm (CMA).A comprehensive theoretical description of the optimisation method is provided. EDAMA, CMA and other equalization algorithms comparative test result are appended.

An Improved T/2 Fractionally Spaced Blind Equalization Algorithm with Coordinate Transform

An Improved T/2 Fractionally Spaced Blind Equalization Algorithm with Coordinate Transform

Low-Complexity Hybrid Adaptive Blind Equalization Algorithm for High-Order QAM Signals

It is well known that the constant modulus algorithm (CMA) presents a large steady-state mean-square error (MSE) for high-order quadrature amplitude modulation (QAM) signals. In this paper, we propose a low-complexity hybrid adaptive blind equalization algorithm, which augments the CMA error function with a novel constellation matched error (CME) term. The most attractive advantage of the proposed algorithm is that it is computationally simpler than concurrent CMA and soft decision-directed (SDD) scheme (CMA+SDD), and modified CMA (MCMA), while the approximation of steady-state MSE of the proposed algorithm is same with CMA+SDD, and lower than MCMA. Extensive simulations demonstrate the performance of the proposed algorithm.