In this paper, Discrete Kalman filter is used as an equalizer for digital transmission because it suppresses the noise and intersymbol interference (ISI). Kalman equalizer is based on the least square optimization cri...
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In this paper, Discrete Kalman filter is used as an equalizer for digital transmission because it suppresses the noise and intersymbol interference (ISI). Kalman equalizer is based on the least square optimization criterion and its structure is made adaptive by combining it in parallel with adaptive algorithms to estimate the channel coefficients. Least mean square (LMS) and Recursive Least Square (RLS) adaptive algorithms are used for identification of channel coefficients and have been implemented to study the performance of Kalman Equalizer. The performance index, Bit Error Rate (BER), of Kalman Equalizer with RLS and LMS adaptive algorithms named K-RLS and K-LMS respectively is studied via computer simulation for different SNR scenario. Simulation results shows that the performance of K-RLS has less BER as compared to that of K-LMS.
The efficient and accurate simulation of material systems with defects using atomistic-to-continuum (a/c) coupling methods is a topic of considerable interest in the field of computational materials science. To achiev...
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This paper presents a review on Acoustic echo cancellation algorithms that are used to update the coefficients of filter which is used as an adaptive echo canceller. The acoustic echo generated in the echo route, when...
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This paper presents a review on Acoustic echo cancellation algorithms that are used to update the coefficients of filter which is used as an adaptive echo canceller. The acoustic echo generated in the echo route, when a microphone gets a speaker signal and sends it to the transmitter. An adaptive filter can minimize the effect of nonlinearity and echo caused due to the nonlinearity of the speaker and other components. The least mean square (LMS) based algorithms, along with those based on metaheuristic and stochastic optimization techniques, are the most widely used adaptive filtering algorithms. Because of their ease of implementation and computation, the gradient based algorithms suffer rare search of global minima solution. To overcome this issue, meta-heuristic and stochastic optimization algorithms are capable of avoiding local minima. To solve the AEC problem, various meta-heuristic approaches were used. The existing algorithms for the echo cancellation of acoustic signals are presented in this paper.
adaptive receiver algorithms are considered for the demodulation of code-division multiple-access (CDMA) signals. These algorithms include neural-network based algorithms and algorithms adapted from linear channel equ...
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adaptive receiver algorithms are considered for the demodulation of code-division multiple-access (CDMA) signals. These algorithms include neural-network based algorithms and algorithms adapted from linear channel equalization techniques. Convergence issues are treated, and the performance of various algorithms is compared via computer simulations.
The question of convergence of the regularized one-step adaptive algorithms of Kaczmarz and Nagumo-Noda, used for solving the identification problem, is studied. Estimates of the rate of algorithms convergence are obt...
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adaptive methods for the estimation of unknown system parameters has the advantage of tracking time-varying systems. Identification algorithms for recursive systems produce nonquadratic performance functions. In such ...
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adaptive methods for the estimation of unknown system parameters has the advantage of tracking time-varying systems. Identification algorithms for recursive systems produce nonquadratic performance functions. In such problems it is very difficult to estimate the nature of convergence in a stochastic frame work. Recently, it has been shown that the ensemble mean parameter updating equations of IIR adaptive algorithm can be represented by associated ordinary differential equations (ODEs). A method of solving the ODEs in order to analyze the mean-convergence behavior of these algorithms, given the mean description of the input in the form of power spectral density, has been presented recently. In this paper, this procedure is applied to study the convergence behavior of recursive adaptive algorithms applied for the identification of pole-zero systems. Effectiveness of this method is shown through analytical and simulation results.
The design of adaptive algorithms for the tracking of slowly time‐varying systems is investigated. A criterion for measuring the tracking capability of an algorithm in this situation was introduced in an earlier work...
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Smart antenna technology provides directed radiation pattern towards desired direction, reduced interference better capacity to wireless communication system. adaptive algorithms are used by smart antenna to compute t...
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ISBN:
(纸本)9781509063147;9781509063130
Smart antenna technology provides directed radiation pattern towards desired direction, reduced interference better capacity to wireless communication system. adaptive algorithms are used by smart antenna to compute the complex weight coefficients in a way that it directs main lobe towards desired user and places null towards undesired user. This paper presents comparative analysis of least mean square (LMS), decision feedback equalizer based least mean square (DFELMS) and data reusing least mean square (DRLMS) algorithms on the basis of beam pattern. Implementation is done with the help of *** results shows that DRLMS algorithm has better performance than LMS and DFELMS algorithms.
In this communication, fast adaptive algorithms are suggested to enhance the performance of the Fast-Normalized Least Mean Square (FNLMS) algorithm in Acoustic Echo Cancellation (AEC) applications with a sparse system...
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ISBN:
(纸本)9781728101125
In this communication, fast adaptive algorithms are suggested to enhance the performance of the Fast-Normalized Least Mean Square (FNLMS) algorithm in Acoustic Echo Cancellation (AEC) applications with a sparse system. We propose two new algorithms, the first one is the Zero-Attracting (ZA) FNLMS which gives a better performance when the unknown system is extremely sparse. However, by decreasing the sparsity of the system, the Mean Square Error (MSE) got significantly worse than that of the FNLMS algorithm. To overcome this issue, another algorithm named Reweighted Zero-Attracting FNLMS (RZA-FNLMS) algorithm is proposed in this paper. Simulation results with stationary and non-stationary inputs under different Signal to Noise Ratio (SNR) values of additive noise and change in the impulse response lengths show an improvement in the convergence speed.
This paper is concerned with new fast adaptive beamforming algorithms which are based on the internal model principle, to deal with fast fading environment to track the desired signal changes and suppress the interfer...
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ISBN:
(纸本)1424403081
This paper is concerned with new fast adaptive beamforming algorithms which are based on the internal model principle, to deal with fast fading environment to track the desired signal changes and suppress the interferences. The LMS and RLS algorithms for constructing nulls are derived by incorporating an AR model into the weight dynamics, and then can track fast fading environment. The convergence analysis of mean and mean-square error of the weight vector are also investigated and stability condition is clarified. Numerical simulation results show that the proposed algorithms can improve tracking ability and BER performance in DS-CDMA systems.
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