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Due to its simplicity the adaptive least mean square (LMS) algorithm is widely used in code-division multiple access (CDMA) detectors. However its convergence speed is highly dependent on the eigenvalue spread of the input covariance matrix. For highly correlated inputs the LMS algorithm has a slow convergence which require long training sequences...

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This paper is about an efficient implementation of adaptive filtering for echo cancelers. First, a realization of an improved Block Proportionate Normalized Least Mean Squares (BPNLMS + +) using Generalized Sliding Fermat Number Transform (GSFNT) is presented. Unfortunately, during the double-talk mode, the echo cancelers often diverge. We can cope...

## Citations

... In order to overcome this problem, various variable step-size (VSS) strategies have been suggested, which have a high step-size initially for fast convergence but then reduce the step-size with time in order to achieve a low error performance [2]- [25]. Some algorithms are proposed in literature for specific applications [12], [14], [15], [20]- [25]. There are several algorithms that are derived from a constraint on the cost function [2], [7], [8], [10], [14]. ...

Several variable step-size strategies have been suggested in the literature to improve the performance of the least-mean-square (LMS) algorithm. Although they enhance performance, a major drawback is the complexity in the theoretical analysis of these algorithms. Researchers use several assumptions to find closed-form analytical solutions. This work presents a unified approach for the analysis of variable step-size LMS algorithms. The approach is then applied to several variable step-size strategies, and theoretical and simulation results are compared.

... Secondly, the trained based implementation which is used when the spreading code and the channel parameters are known or can be estimated. In such case, a known training sequence is transmitted which is used to tune the coefficients of the adaptive filter before the actual data is sent [4]. Thus, the receiver converges to its steady state, and thereafter it can be made to run in a decision directed mode. ...

This paper proposes an enhancement to the performance of a Direct Sequence Code Division Multiple Access (DS-CDMA) system by utilizing an adaptive filter in the presence of different jamming techniques. In order to combat the impact of such jamming, the adaptive filter utilizes three adaptive algorithms which are the Variable Step-Size Affine Projection (VSS-APA) algorithm, the Generalized Normalized Gradient Descent (GNGD) algorithm, and the Generalized Square-Error-Regularized (GSER) NLMS algorithm. These algorithms have the advantages of fast convergence, low steady state mean squared error and the ability to improve the bit error rate (BER) performance of the conventional CDMA system, in the presence of multi-path, multiple-access, and different jamming signals. Results show that the VSS-APA outperforms other algorithms in the presence of barrage jamming. Whereas in the presence of partial band jamming the GSER-NLMS adaptive filter gives the best performance.

... They analyzed the alternating odd/even partial update LMS algorithm and derived stability bound on step size parameter for wide sense stationary and cyclo-stationary signals based on external properties of the matrix 2-norm but comparing with the proposed model, the memory load and computational complexity is still large. The behavior of three variants of variable step size LMS algorithm for training based multi-user detection in a CDMA system was studied by [10]. Two of the algorithm have smaller computational complexity and memory load but still suffers from the fact that their steady state error and speed of convergence depend on the same parameters (the step size), therefore complementary pair variable step size LMS was introduced. ...

Channel estimation is an important and necessary function performed by modern wireless receivers. The goal of channel estimation is to measure the effects of the channel on known or partially known transmission. The usual practice in acquiring knowledge about a channel is to model the channel and then acquire the parameters involved in the model. This paper proposes a variable partial update model for adaptive communication channel estimation with a view to improving signal error at the receiver station. The proposed model is composed of finite impulse response transversal adaptive filter and least mean square adaptation algorithm. The performance of the proposed model was compared with the full update model. The evaluation results indicated that the proposed model performed better than the full update model in terms of computational complexity, memory load, and convergence rate.

In this paper we introduce a numeric method to resolve a non-contact voltage sensor equation and track the original voltage in the measured surface for medium and high alternated voltage systems. We introduce the non-contact sensor itself, its principle of operation and equations, and also the method we use to increase the sensitivity of the measurement. The numeric method is then presented in detail, as well as the simulation results and an assessment of accuracy. We were able to achieve very good results with the presented technique, with the best results falling under 1% of estimation error for the fundamental frequency, as well as for the 3 rd , 5 th and 7 th harmonics.

In the paper, we propose a new CSS(Complex Signed-Signed) CMA(Constant Modulus Algorithm) algorithm for ICS(Interference Cancellation System). When the repeater get the feedback signal, the CSS CMA algorithm is proposed at the ICS repeater using DSP(Digital Signal Processing) for the removal of interfering signals from the feedback paths. The proposed CSS CMA algorithm improved performances and hardware complexity by adjusting step size values. the steady state MSE(Mean Square Error) performance of the proposed CSS CMA algorithm with step size of 0.00043 is about 4dB better than the conventional CMA algorithm. And the proposed Complex Signed Signed CMA algorithm requires 1950 ~ 2150 less iterations than the LMS(Least Mean Square) and Signed LMS(Normalized Least Mean Square) algorithms at MSE of -25dB.

In the paper, we propose a new CMF(Constant Modulus Fourth) algorithm for WCDMA(Wideband Code Multiple Access) RF(Radio Frequency) Repeater. CMF algorithm is proposed by modifying the CMA(Constant Modulus Algorithm) algorithm and improved performances are achieved by properly adjusting step size values. The steady state MSE(Mean Square Error) performance of the proposed CMF algorithm with step size of 0.35 is about 4dB better than that of the conventional CMA algorithm. And the proposed CMF algorithm requires 400~1100 less iterations than the LMS(Least Mean Square) and NLMS(Normalized Least Mean Square) algorithms at MSE of -25dB.

In this article, we proposed a new interference canceller using the adaptive algorithm. We designed constant modulus algorithm-correlation normailized least mean square (CMA-CNLMS) for wireless system. This structure is normalized LMS algorithm using correlation between the desired and input signal for cancelling the interference signals in the wideband code division multiple access (WCDMA) system. We showed that the proposed algorithm could improve the Mean Square Error (MSE) performance of LMS algorithm. MATLAB (Matrix Laboratory) is employed to analyze the proposed algorithm and to compare it with the experimental results. The MSE value of the LMS with mu=0.0001 was measured as - 12.5 dB, and that of the proposed algorithm was -19.5 dB which showed an improvement of 7dB.

Adaptive inverse control of linear system with fixed learning rate least mean square (LMS) algorithm is improved by varying the learning rate. This variable learning rate LMS algorithm is proved to be convergent by using Lyapunov method. It has better performance especially when there is noise in command input signal. And it is simpler than the Variable Step-size Normalized LMS algorithm. A water box temperature control example is quoted in this paper. Simulation results are carried out and show that the adaptive inverse control with variable learning rate LMS is better than that with the fixed learning rate LMS algorithm and the Variable Step-size Normalized LMS algorithm.

Variable Step-size LMS algorithms (VS LMS) have been widely applied to the inverse modeling of an unknown plant in linear adaptive inverse control system due to their advantages over standard LMS in reducing the trade-off between the convergence speed and steady-state error. Plant dynamics, however, can cause eigenvalue spread in the autocorrelation matrix of the controller’s input signal, resulting in slow convergence of the plant inverse model and hence long training sequence. This paper analyzes and compares a class of VS LMS algorithms for linear adaptive inverse control and shows that the Variable Step-size NLMS (VS NLMS) algorithm highly increases the convergence rate while remaining low misadjustment error.