Blind Channel Equalization Using Adaptive Signal Processing Algorithms (original) (raw)
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Blind Channel Equalization by Adaptive Filter Algorithms
Universal Journal of Applied Mathematics, 2017
This paper propose an algorithm based on ZF and MMSE methods for blind channel equalization, which is compared with adaptive filter algorithms which are Constant Modulus Algorithm (CMA), Fractional Space CMA (FSCMA) and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA). The simulations show that the proposed algorithm gives satisfied result versus CMA, FSCMA and SKMAA algorithms. The study is done under certain conditions, it is implemented in noisy environment, for different number of symbols and different SNR values with QPSK modulation. Equalization of channel is more performing if we use the proposed algorithms.
— The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the NLMS algorithm. We present in this paper an multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the standard fractionally spaced CMA (constant modulus algorithm) equalizer in the presence of noise, we show that CMA local minima exist near the minimum mean-square error (MMSE) equalizers. Consequently, Fractional Spaced CMA may converge to a local minimum corresponding to a poorly designed MMSE receiver with considerablely large mean-square error. The step size in the NLMS algorithm decides both the convergence speed and the residual error level, the highest speed of convergence and residual error level.
Comparative Study of Blind Channel Equalization
In this paper we present two algorithms for blind channel equalization. These algorithms are compared with the adaptive filter algorithms such as Constant Modulus Algorithm (CMA), Fractional Space CMA (FSCMA) and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA). The simulation results in noisy environment and for different number of symbols show that the presented algorithms gives good results compared to CMA, FSCMA and SKMAA algorithms. The channel equalization is performed using the ZF and MMSE algorithms.
Performance Comparison of Adaptive and Blind Equalization Algorithms for Wireless Communication
Bonfring
Adaptive equalization is a well known method to minimize the Inter-Symbol Interference (ISI) in wireless communication. Often, adaptive algorithm requires transmission of known training sequence to track the time varying characteristics of the channel and hence utilizes additional bandwidth. It is also impractical to have training sequences in all types of transmissions (e.g. non-cooperative environment). Blind algorithm is a concept to track the time varying characteristics of the channel in the absence of training sequence. However, it leads to slow convergence. In this paper, we compare the performance of adaptive LMS algorithm and SATO based blind algorithm for PAM signal.
PERFORMANCE COMPARISONS OF ADAPTIVE ALGORITHMS FOR BLIND EQUALIZATION
In this paper, we compare several algorithms for blind channel equalization. The analysis includes the joint order detection and blind channel estimation by least squares smoothing (J-LSS), the adaptive version of the J-LSS algorithm, and the prediction error algorithm. Analysis are performed with respect to the computa-tional complexity and convergence speeds of the algo-rithms.
International Journal of Advanced Computer Science and Applications, 2012
The recent digital transmission systems impose the application of channel equalizers with bandwidth efficiency, which mitigates the bottleneck of intersymbol interference for high-speed data transmission-over communication channels. This leads to the exploration of blind equalization techniques that do not require the use of a training sequence. Blind equalization techniques however suffer from computational complexity and slow convergence rate. The Constant Modulus Algorithm (CMA) is a better technique for blind channel equalization. This paper examined three different error functions for fast convergence and proposed an adaptive blind equalization algorithm with variable step size based on CMA criterion. A comparison of the existing and proposed algorithms' speed of convergence shows that the proposed algorithm outperforms the other algorithms. The proposed algorithm can suitably be employed in blind equalization for rapidly changing channels as well as for high data rate applications.
An Overview of Adaptive Channel Equalization Techniques and Algorithms
2014
Wireless communication system has been proved as the best for any communication. However, there are some undesirable threats of a wireless communication channel on the information transmitted through it, such as attenuation, distortions, delays and phase shifts of the signals arriving at the receiver end which are caused by its band limited and dispersive nature. One of the threats is ISI (Inter Symbol Interference), which has been found as a great obstacle in high speed communication. Thus, there is a need to provide perfect and accurate technique to remove this effect to have an error free communication. Thus, different equalization techniques have been proposed in literature. This paper presents the equalization techniques followed by the concept of adaptive filter equalizer, its algorithms (LMS and RLS) and applications of adaptive equalization techniques.
Comparative Study of Adaptive Filtering Algorithms and the Equalization of Channel
2016
Introduction In modern digital communications, it is well known that channel equalization plays an important role in compensating channel distortion. Unfortunately, various channels have time varying characteristic and their transfer functions change with time. Furthermore, time-varying multipath interference and multiuser interference are two major limitations for high speed digital communications. Usually, adaptive equalizers are applied in order to cope with these issues [15]. For adaptive channel equalization, we need a suitable filter structure and proper adaptive algorithms. High-speed digital transmissions mostly suffer from inter-symbol interference (ISI) and additive noise. The adaptive equalization algorithms recursively determine the filter coefficients in order to eliminate the effects of noise and ISI. Adaptive filtering [6] is based on finding optimal parameters by minimizing a performance criterion. Frequently, this minimization is done by seeking the least squares. T...
Decision directed adaptive blind equalization based on the constant modulus algorithm
Signal, Image and Video Processing, 2007
In this paper, new decision directed algorithms for blind equalization of communication channels are presented. These algorithms use informations about the last decided symbol to improve the performance of the constant modulus algorithm (CMA). The main proposed technique, the so called decision directed modulus algorithm (DDMA), extends the CMA to non-CM modulations. Assuming correct decisions, it is proved that the decision directed modulus (DDM) cost function has no local minima in the combined channel-equalizer system impulse response. Additionally, a relationship between the Wiener and DDM minima is established. The other proposed algorithms can be viewed as modifications of the DDMA. They are divided into two families: stochastic gradient algorithms and recursive least squares (RLS) algorithms. Simulation results allow to compare the performance of the proposed algorithms and to conclude that they outperform well-known methods.