Blind Equalization Algorithm Research Articles

고차 QAM 신호에 대한 결정 지향 오차 신호의 크기 값을 이용한 이중 모드 블라인드 등화 알고리즘의 성능 분석

In this paper, we propose a dual-mode blind equalization algorithm that two of the blind equalization algorithm using the size of the decision-directed error signal is automatically switched. The proposed algorithm has a faster convergence speed due to operation of the MSAGF-SMMA with large fixed step-size mainly in the initial equalization. After the equalization has been made to some extent, the proposed algorithm has a smaller residual error in the steady- state by operation of the MSAGF-SMMA with a variable step-size mainly. The variable step-size is determined by multiplying the size of the decision-directed error signal of a fixed step-size. In this paper, we analyze the performance of the proposed algorithm. The computer simulation results demonstrate that the proposed algorithm has a significantly improved performance in terms of a residual inter-symbol interference and residual error in the steady-state compared with the MMA, SMMA, and MSAGF-SMMA.

Sign algorithm for blind channel equalisation

A new admissible hardware-efficient adaptive blind sign equalisation algorithm for quadrature amplitude modulation systems is presented. The main idea is to amplify the equalised sequence while constraining it to not to exceed the largest signal amplitude. Simulation results demonstrate improved performance in terms of mean squared error and inter-symbol interference measures.

Research on Ant Colony Algorithm Optimization Neural Network Weights Blind Equalization Algorithm

The project of ant colony algorithm optimization neural network combining blind equalization algorithm is proposed. The better initial weights of neural networks are provided because of the randomness, ergodicity and positive feedback of the ant colony algorithm. And then, a combination of optimal weights are found through BP algorithm, which is fast local search speed. Thus blind equalization performance is improved. Computer simulation show that, the novel blind equalization algorithm speeds up the convergence rate, reduces the remaining steady-state error and bit error rate, which is compared with the Neural Network Blind Equalization Algorithm(NNBE) and Genetic Algorithm optimization Neural Network Blind Equalization Algorithm(GA-NNBE) .

Second Order Statistics Driven LMS Blind Fractionally Spaced Channel Equalization

A novel cooperative least mean square adaptation rule is proposed with application to fractionally spaced blind channel equalization schemes used in quadrature amplitude modulation data links. A novel cost function based on second-order statistics only of the received signal is defined and jointly used with another chosen among classical blind cost functions such as decision directed and other Bussgang-type ones. Numerical results show that severe misconvergence observed in classical blind equalization algorithms is drastically mitigated using the here described technique.

Demultiplexing based on frequency-domain multimodulus blind equalization for mode division multiplexing system

In this paper, the frequency-domain multimodulus blind equalization algorithm (FD-MMA) is proposed to implement mode demultiplexing for a 2 × 2 mode division multiplexing system. The performance of this algorithm is compared with the time-domain MMA (TD-MMA) and frequency-domain decision-directed least mean square algorithm (FD-LMS). Compared with TD-MMA, the FD-MMA reduces the computational complexity significantly while maintaining the same performance. Compared with FD-LMS, the FD-MMA has a better performance in BER with similar computational complexity. As for 16QAM format, the average OSNR of FD-MMA improves 2.5 dB at a BER of 10−3; as for 64QAM, it has an improvement of 7 dB.

Sequential Bayesian Algorithms for Identification and Blind Equalization of Unit-Norm Channels

In many estimation problems of interest, the unknown parameters reside on spherical manifolds. As most common filtering algorithms assume that parameters have Gaussian prior distributions, their application to such problems leads to suboptimal performance. In this letter, we propose a model in which the unknown unit-norm parameter vectors have Fisher–Bingham (F-B) prior distributions. We show that if the observations relate to the parameters via Gaussian likelihoods, the F-B priors form a conjugate model that yields closed-form, recursive estimators that naturally take into account the restrictions on the unknowns. We apply this model to a communication setup with multiple gain-controlled FIR frequency-selective channels, deriving a novel maximum a posteriori (MAP) channel parameter estimator and a blind equalizer based on Rao–Blackwellized particle filters. As we verify via Monte Carlo numerical simulations, the F-B model leads to superior performance compared to previous algorithms that adopt mismatched Gaussian prior models.

Performance analysis of a family of adaptive blind equalization algorithms for square-QAM

Multimodulus algorithms (MMA) based adaptive blind equalizers mitigate inter-symbol interference and recover carrier-phase in communication systems by minimizing dispersion in the in-phase and quadrature components of the received signal using the respective components of the equalized sequence in a decoupled manner. These equalizers are mostly incorporated in bandwidth-efficient digital receivers which rely on quadrature amplitude modulation (QAM) signaling. The nonlinearities in the update equations of these equalizers tend to lead to difficulties in the study of their steady-state performance. This paper presents originally the steady-state excess mean-square-error (EMSE) analysis of different members of multimodulus equalizers MMAp–q in a non-stationary environment using energy conservation arguments, and thus bypassing the need for working directly with the weight error covariance matrix. In doing so, the exact and approximate expressions for the steady-state mean-square-error of several MMA based blind equalization algorithms are derived, including MMA2–2, MMA2–1, MMA1–2, and MMA1–1. The accuracy of the derived analytical results is validated using Monte–Carlo experiments and found to be in close agreement.

Performance Comparison of Variable Step Size Techniques of Sato and Godard-Based Blind Equalizer

The modern digital high speed wireless communication system demands quick convergence rate and low steady state error. The balancing between the demands can be achieved by opting step size. Thus, it is essential to define new algorithms to equalize channels and mitigate noise in communications. It is renowned that time varying step size blind equalization technique can speed up the convergence rate and minimize the misadjustment. This work presents a variable step size (VSS) approach based on godard blind equalization algorithm to resolve the conflict between the convergence rate and precision of the fixed step-size godard algorithm. The results of this projected approach is compared with the existing variable step size sato algorithm for a pulse amplitude modulated (PAM) input symbol.

A Fast Converge Momentum Dual-Mode Modified Constant Modulus Blind Equalization Algorithm for High-Order QAM Signals

A dual-mode blind channel equalization algorithm for high-order quadrature amplitude modulation (QAM) signals is proposed. The proposed algorithm, named momentum constellation matching error modified constant modulus algorithm (MCME---MCMA), firstly starts the equalization by momentum MCMA algorithm, and then introduces a constellation matching error term when it beginning to converge, which significantly reduces the steady-state mean square error (MSE). An optimized switching-point is provided and confirmed by simulations. Simulations demonstrate that MCME---MCMA improves the convergence rate and decreases the MSE by 5---7 dB for 64-QAM and 256-QAM compared with that of MCMA.

CME 오차와 non-CME 오차의 선형 결합에 의해 제어되는 적응 블라인드 등화

이 논문에서는 신호점 매칭 오차(constellation-matched error: CME)와 비 신호점 매칭 오차(non-constellation-matched error: non-CME)를 선형 결합한 오차 신호 기반의 블라인드 등화 알고리즘을 제안한다. 새로운 오차 신호는 초기 수렴을 달성하기 위한 non-CME 항과 출력 신호의 심볼간 간섭(intersymbol interference: ISI) 성능을 개선하기 위한 CME 항을 포함하도록 설계되었고, 결합 인자를 통해 두 오차 항을 제어한다. 오차 항을 제어하여 등화 단계에 적합한 오차 신호를 발생함으로써 기존 알고리즘에 비해 수렴 속도와 심볼간 간섭 제거 성능을 개선하였다. 다중경로 채널에 잡음을 부가한 조건하에서 이루어진 64-QAM과 256-QAM 신호에 대한 모의실험에서 제안 방법이 CMA와 CMA+DD 동시 등화에 비해 우수한 것으로 나타났다.

Sixth-Second Order Normalized Cumulants Blind Equalization Algorithm Based on T/4 Oversampling

Most existing blind equalization algorithms rely on partial or complete channel identification, but the channel order estimation is always a difficult task. In this paper, higher order normalized cumulants analysis is applied to the blind equalization, and a new sixth-second order normalized cumulants blind equalization algorithm based on oversampling is proposed. The proposed method recovers the transmitted sequence adopting optimization algorithm of sixth-second order normalized cumulants without channel identification and channel order estimation. Simulation results show the algorithm's effectiveness.

Normalized p-Norm Blind Equalization Algorithm with Adaptive Momentum under Impulsive Noise Environment

Hereby a normalized p-norm blind equalization algorithm with adaptive momentum was proposed. Normalized p-norm LMS-CMA can obtain robust convergence performance under impulsive noise environment, however, the convergence rate is slow. To further improve the performance of the normalized p-norm LMS-CMA, adaptive momentum according to the instantaneous error is designed. If the instantaneous error based on CMA criterion and DD criterion has the same sign, the momentum factor remains unchanged. Otherwise, the momentum factor is set to 0. The simulation results show that, the proposed algorithm has faster convergence rate than the normalized p-norm blind equalization algorithm, furthermore, it has robust convergence performance under impulsive noise environment.

DNA genetic artificial fish swarm constant modulus blind equalization algorithm and its application in medical image processing.

This study proposes use of the DNA genetic artificial fish swarm constant modulus blind equalization algorithm (DNA-G-AFS-CMBEA) to overcome the local convergence of the CMBEA. In this proposed algorithm, after the fusion of the fast convergence of the AFS algorithm and the global search capability of the DNA-G algorithm to drastically optimize the position vector of the artificial fish, the global optimal position vector is obtained and used as the initial optimal weight vector of the CMBEA. The result of application of this improved method in medical image processing demonstrates that the proposed algorithm outperforms the CMBEA and the AFS-CMBEA in removing the noise in a medical image and improving the peak signal to noise ratio.

Multimodulus blind equalization algorithm based compensation for I/Q imbalance in OFDM optical fiber communication

The performance enhancement using multimodulus blind equalization algorithm for compensating in-phase/ quadrature-phase (I/Q) mismatch in orthogonal frequency division multiplexing optical communication system is investigated. The mathematical theory of I/Q imbalance is analyzed. Then, a compensation scheme with two-tap equalizer is implemented, with this scheme, the joint compensation of transmitter and receiver with optical channel that contains noise attenuation and chromatic dispersion is realized. Simulation shows that the performance degradation caused by I/Q imbalance can be compensated by the scheme with multimodulus blind equalization algorithm effectively regardless of the modulation order and channel distortion.

Widely linear RLS constant modulus algorithm for complex-valued noncircular signals

Based on the constant modulus criterion, a new Widely Linear (WL) blind equalizer and a novel widely linear recursive least square constant modulus algorithm are proposed to improve the blind equalization performance for complex-valued noncircular signals. The new algorithm takes advantage of the WL filtering theory by taking full use of second-order statistical information of the complex-valued noncircular signals. Therefore, the weight vector contains the complete second-order information of the real and imaginary parts to decrease the residual inter-symbol interference effectively. Theoretical analysis and simulation results show that the proposed scheme can significantly improve the equalization performance for complex-valued noncircular signals compared with traditional blind equalization algorithms.

Improved Constant Modulus Algorithm for Blind Equalization of QAM Signals

The paper present new approach for improving the steady-state error performance of Constant Modulus Algorithm (CMA), it is well known that for higher level modulations such as QAM, CMA does not perform well. Several techniques have been proposed in recent years to deal with slow convergence and MSE performance of CMA. Constellation matched error has been seen to offer best performance by providing the cost function with the knowledge of the constellation symbols. New constellation match error function is studied, simulation is performed, it can be witnessed that 4dB improvement stead state error performance.

Flag를 가지는 SMMA 적응 블라인드 등화 알고리즘의 성능 분석

Flag를 가지는 SMMA 적응 블라인드 등화 알고리즘의 성능 분석

Variational Bayesian Inference for Multichannel Dereverberation and Noise Reduction

Room reverberation and background noise severely degrade the quality of hands-free speech communication systems. In this work, we address the problem of combined speech dereverberation and noise reduction using a variational Bayesian (VB) inference approach. Our method relies on a multichannel state-space model for the acoustic channels that combines frame-based observation equations in the frequency domain with a first-order Markov model to describe the time-varying nature of the room impulse responses. By modeling the channels and the source signal as latent random variables, we formulate a lower bound on the log-likelihood function of the model parameters given the observed microphone signals and iteratively maximize it using an online expectation-maximization approach. Our derivation yields update equations to jointly estimate the channel and source posterior distributions and the remaining model parameters. An inspection of the resulting VB algorithm for blind equalization and channel identification (VB-BENCH) reveals that the presented framework includes previously proposed methods as special cases. Finally, we evaluate the performance of our approach in terms of speech quality, adaptation times, and speech recognition results to demonstrate its effectiveness for a wide range of reverberation and noise conditions.

Improved multi‐stage clustering‐based blind equalisation in interference‐limited CDMA environments

The improved multi-stage clustering (IMSC)-based blind equalisation algorithm proposed by Mitra and colleagues in 2011 gave rise to significant improvements in terms of performance metrics as compared with its state-of-the-art counterparts. However, the present authors' research studies show that IMSC performance degrades significantly for multiple access interference (MAI) in code-division multiplexing access (CDMA) systems. To mitigate MAI, code filtering by orthogonal Hadamard codes is a popular practice to minimise the interference. Code-filtering techniques have been used in the past in which a de-correlating transform (inverse of an orthonormal Hadamard matrix) has been incorporated into the cost function of the parent algorithm (which can be any traditional equalisation algorithm). A hybrid equalisation scheme is suggested which combines IMSC and code-filtering, so as to mitigate interference in CDMA environments. The code-filtered IMSC shows better performance than any of the previously proposed algorithms.

The Research of Wavelet Transform Blind Equalization Algorithm Based on Ant Colony Optimization

ACA (Ant Colony Algorithm) is a smart global random search algorithm, which is combined with blind equalization algorithm. In optimizing and initializing equalizer weight vector, ACA can avoid falling into local extremum easily in Stochastic Gradient Descent Algorithm of CMA and improve the convergence speed and reduce the steady-state mean square error greatly. This paper mainly studies the ACA to initialize the equalizer weight vector and the theory of wavelet transform, comes up with the wavelet blind equalization algorithm based on Ant Colony Optimization (ACA-WTCMA).The simulation results of underwater acoustic channel verify the effectiveness of the algorithm.