Robust incremental growing multi-experts network

This research was solely conducted to observe the adaptive filter ability in identification of multipath channel for Wideband Code Division Multiple Access (WCDMA). The method used was limited to a simulation of mutipath channel identification by adaptive filter, which imitated the characteristics of the channel. LMS (Least Mean Square) and NLMS (Normalized Least Mean Square) filter were observed, where as Kalman was used to compare the results. There were some variable to be varied in this simulation, namely the convergence variable, the filter length, and the SNR. Channel identification analysis is based on the estimated channel coefficients compared with channel coefficients, the convergence constant of adaptive filter, the adaptation time, the mean square errors (MSE), and the bit error rates. The results show that NLMS filter has a good performance in channel identification. LMS filter has largest mean square error in the channel identification. Kalman gives more precise results but has complex algorithm. Kalman left the least mean square error, which is 4.1e-34 at 0.16 of convergence rate. All filters have good performance on signal detection in various signal to noise ratio, especially for SNR ³ 10 dB. Bit error rate at 10 dB SNR is 3.33e-4.

A new method for seismic random noise reduction based on robust function and back propagation (BP) neural network is proposed in this paper. This method introduces BP neural network utilizing least mean log squares (LMLS) error function or least trimmed squares (LTS) estimator instead of least mean squares (LMS) error function as its error function. The proposed method can diminish the influence of random noise on the accuracy of BP neural network model and improve the denoising capability of neural network, obviously. Experimental results demonstrate that the proposed new method can reduce random noise on seismic data and preserve in-phase axes more effectively than some traditional denoising methods and generic BP neural network model.

The Least Mean Square (LMS) algorithm has a slow convergence rate as it is dependent on the eigenvalue spread of the input correlation matrix. In this research, we solved this problem by introducing a novel adaptive filtering algorithm for complex domain signal processing based on q-derivative. The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. The proposed algorithm could be considered as an extension of the q-LMS algorithm for the complex domain. Transient and steady-state analyses of the proposed q-CLMS algorithm are performed and exact analytical expressions for mean analysis, mean square error (MSE), excess mean square error (EMSE), mean square deviation (MSD) and misadjustment are presented. Extensive experiments have been conducted and a good match between the simulation results and theoretical findings is reported. The proposed q-CLMS algorithm is also explored for whitening applications with satisfactory performance. A modification of the proposed q-CLMS algorithm called Enhanced q-CLMS (Eq-CLMS) is also proposed. The Eq-CLMS algorithm eliminates the need for a pre-coded value of the q-parameter thereby automatically adapting to the best value. Extensive experiments are performed on system identification and channel equalization tasks and the proposed algorithm is shown to outperform several benchmark and state-of-the-art approaches namely Complex Least Mean Square (CLMS), Normalized Complex Least Mean Square (NCLMS), Variable Step Size Complex Least Mean Square (VSS-CLMS), Complex FLMS (CFLMS) and Fractional-ordered-CLMS (FoCLMS) algorithms.

MIMO-OFDM Systems are widely used nowadays because of their improved performance in terms of link reliability, high data rates and capacity. Channel Estimation is one of the major challenges faced by a MIMO-OFDM system. For a rapidly time varying wireless channel, Adaptive Channel Estimation (ACE) algorithms are widely used for the purpose of channel estimation. Least Mean Square (LMS) algorithm is the commonly used ACE algorithm, as it has low complexity and numerical robustness. Its main disadvantage is high Mean Square Error (MSE). This disadvantage is overcome in Normalized LMS algorithm with low MSE but its complexity is high and convergence performance is poor. Inorder to further reduce the complexity of LMS algorithm, several simplified LMS algorithms can be used. Simplified algorithms includes Sign Data LMS (SDLMS) algorithm, Sign Error LMS (SDLMS) algorithm, Sign Data Normalized LMS (SDNLMS) algorithm, Sign Data Sign Error Least Mean Square (SDSELMS) algorithm etc. In all these algorithms there occurs a trade-off between convergence rate and MSE. In order to overcome this trade-off, a Variable Step Size (VSS) algorithm can be used. By combining VSS algorithm with such simplified algorithms, we can realize CE algorithms that can reduce the complexity as well as the trade-off between convergence rate and MSE performance. In this paper, a new fast convergence, low complex Variable Step Size-Sign Data Sign Error Least Mean Square (VSS-SDSELMS) algorithm is proposed which further improves the convergence performance along with low computational complexity and comparable MSE performance.

Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.

Recent results demonstrate that adaptive filters implemented with the least mean square (LMS) algorithm can exhibit better mean square error (MSE) performance than the corresponding Wiener filter. We examine some conditions under which this can occur for implementations of an adaptive noise canceler and an adaptive equalizer. In particular, we demonstrate that because of the recursive LMS update equation, the LMS estimator is non-linear and uses much more information than that used by the corresponding Wiener filter. Under certain circumstances, this extra information can enhance the MSE performance of LMS over the Wiener filter. To quantify this effect, we use a transfer function approach to approximate the MSE of the LMS estimator. We also show that the LMS estimator is indeed bounded in MSE performance by a linear Wiener filter that explicitly uses the same information used in the LMS estimator.

본 논문에서는 RF(Radio Frequency) 중계기에서 그룹화 CMA(Constant Modulus Algorithm)와 LMS(Least Mean Square) 알고리즘을 이용하여 적응 필터를 적용시킨 새로운 혼합 간섭 제거기를 제안한다. 송신 안테나에서 수신안테나로 궤환되는 신호는 수신 시스템의 성능을 저하시킨다. 제안한 간섭 제거기는 그룹화 CMA 알고리즘 간섭 제거 기법을 적용시키기 때문에 기존 구조보다 나은 채널 적응 성능과 낮은 MSE(Mean Square Error)을 가진다. 이 구조는 기존 비선형 간섭 제거기에 비해 같은 MSE(Mean Square Error)에 대한 반복수와 하드웨어 복잡도를 줄여준다. 즉, 제안한 알고리즘은 LMS 알고리즘에 비해 평균 자승 에러가 적응 상수에 따라 2.5 dB 또는 4 dB 정도 낮은 값을 보였다. 또한, VSS(Variable Step Size)-LMS 알고리즘에 비해 수렴 속도가 빠르고, 비슷한 평균 자승 에러를 가진다. In this paper, we proposed a new hybrid interference canceller using the adaptive filter with Grouped CMA(Constant Modulus Algorithm)-LMS(Least Mean Square) algorithm in the RF(Radio Frequency) repeater. The feedback signal generated from transmitter antenna to receiver antenna reduces the performance of the receiver system. The proposed interference canceller has better channel adaptive performance and a lower MSE(Mean Square Error) than conventional structure because it uses the cancellation method of Grouped CMA algorithm. This structure reduces the number of iterations fur the same MSE performance and hardware complexity compared to conventional nonlinear interference canceller. Namely, MSE values of the proposed algorithm were lower than those of LMS algorithm by 2.5 dB and 4 dB according to step sizes. And the proposed algorithm showed fast speed of convergence and similar MSE performance compared to VSS(Variable Step Size)-LMS algorithm.

This paper surveys recent advances related to sparse least mean square (LMS) algorithms. Since standard LMS algorithm does not take advantage of the sparsity information about the channel being estimated, various sparse LMS algorithms that are aim at outperforming standard LMS in sparse channel estimation are discussed. Sparse LMS algorithms force the solution to be sparse by introducing a sparse penalty to the standard LMS cost function. Under the reasonable conditions on the training datas and parameters, sparse LMS algorithms are shown to be mean square stable, and their mean square error performance and convergence rate are better than standard LMS algorithm. We introduce the sparse algorithms under Gaussian noises model. The simulation results presented in this work are useful in comparing sparse LMS algorithms against each other, and in comparing sparse LMS algorithms against standard LMS algorithm.

Electrocardiogram (ECG) signals are affected by various types of noises that are differed based on frequency content. In order to improve accuracy and reliability, it is essential to remove such a disturbance. The denoising of ECG signals is challenging as it is difficult to apply filters with fixed coefficients. Adaptive filtering techniques can be used, in which the filter coefficients can be modified to record the dynamic changes of the signal. The system changes with a sparsity level such as non-sparse, semisparse and sparse. A new approach combination of Least Mean Square (LMS) and Zero Attractor LMS (ZA-LMS) filter is proposed to be suitable for sparse and also for non-sparse environments. It also classifies the system which adjusts to the sparseness level of the system. But later LMS filter was modified with Error Nonlinear Sign LMS (ENSLMS) filter with help of this, SNR gets improved. The performance of these algorithms is simulated using Xilinx system generator and the obtained SNR's are compared.

Mean square deviation analysis of LMS and NLMS algorithms with white reference inputs

A major complaint of individuals with normal hearing and hearing impairments is a reduced ability to understand speech in a noisy environment. This paper is proposed to remove the noise from the speech signal in various real time environments and to make the hearing impaired to understand the information by transforming speech in to text format. The quality of audio signal can be improved by filtering the degraded speech signal through adaptive filters. For Noise cancellation widrow & hoff’s Least Mean Square (LMS) algorithms are being used for simplicity in implementation. The LMS algorithm was simple in it's derivation and robust in a number of applications but it also have limitation in selection of a certain values such as step size and results in computational complexity. Hence to overcome all the limitations and to present an enhanced speech signal, a novel approach named VSSNDLMS algorithm is proposed. The performance of algorithms like Variable Step Size Normalised LMS , Normalised Differential LMS with proposed VSSNDLMS with different input signals are analysed in this paper . Finally, through simulation results the proposed VSSNDLMS algorithm converges fastly with minimum mean square error and it is useful in predicting the adaptive filter performance of various algorithms and the implementation indicate the improvement in quality of the speech signal and it appears to be favourable for hearing impaired. Keywords: VSSNDLMS, LMS Algorithm, Noise Cancellation, Speech Processing, Adaptive Filter

본 논문에서는 8VSB 기반의 DTV 방송시스템에서 동일 채널 중계기에 적용 가능한 등화기 알고리즘에 대해 분석하고, 이를 통해 궤환신호에 의한 에러 전파를 감소 시키는 동시에, 수신 성능을 향상 시킬 수 있는 있는 등화기 구조를 제안한다. 궤환 신호의 효율적 제거 여부를 확인하기 위하여 LMS (Least Mean Square) 기반의 DFE (Decision Feedback Equalizer) 와 Correlation LMS 등의 알고리즘 분석을 통해, 이와 연동할 수 있는 다중레벨의 상관 LMS 기법을 제안하고, 이를 기존 방식들과 비교해 봄으로써 효율적으로 에러전파 현상을 크게 감소 시키는 것을 확인할 수 있었다. 컴퓨터 모의실험 수행시, DTV 방송 시스템 환경에 널리 사용되는 브라질 채널 모델을 적용하여 등화기 알고리즘을 서로 비교 분석하여 그 결과를 도출하였다. DTV 방송시스템에서 동작 수신 SNR 값인 15~25dB 까지 범위에서의 심볼 에러율 및 MSE (Mean Square Error) 등을 살펴보았다. 기존의 방식들과 비교해 본 결과 제안방식이 동일한 비트 에러 오류 정정 성능을 유지하는데 필요한 신호대 잡음비가 약 2~5 dB 정도 감소 했고, MSE 를 통한 수렴속도 측면에서도 필요시간이 감소하였음을 알 수 있었다. In this paper, the equalizer techniques that is able to adopt the digital on-channel repeater for 8VSB-based DTV system has been analyzed and we propose an effective equalizer structure which can reduce the error propagation phenomenon by the feedback signal and improve the receiver performance at the same time. In order to confirm the effective cancellation of the feedback signal, the multi-level Correlation LMS scheme is proposed through the analysis of conventional basic LMS based DFE and Correlation LMS algorithm and as compared with the conventional method, we can confirm the reduction of error propagation. When performing the computer simulation, as the Brazil channel model which is very popular for DTV broadcasting system is adopted, the result is drawn by comparing and analysing the equalizer algorithm. We have examine the symbol error rate which is in the range of 15~25dB of operation receipt SNR and MSE(Mean Square Error) in the DTV broadcasting system. As a result of comparing with the existing method, the signal-noise ratio which is necessary for maintain the bit error correction ability that the means of proposal is same is reduced by about 2~5dB, and in the rate of convergence through the MSE, we found the reduction of needed time.

In the paper, we propose a new Signed LMS(Least Mean Square) algorithm for ICS(Interference Cancellation System). The proposed Signed LMS algorithm improved performances by adjusting step size values. At the convergence of 1000 iteration state, the MSE(Mean Square Error) performance of the proposed Signed LMS algorithm with step size of 0.067 is about 3 ∼ 18 dB better than the conventional LMS, CMA algorithm. And the proposed Signed LMS algorithm requires 500 ∼ 4000 less iterations than the and LMS and CMA algorithms at MSE of -25dB.

Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Equalization is a most widely used optimization scheme to reduce inter-symbol interference (ISI). Inter symbol interfering environment is a case of misrepresentation of the sent signal at the receiver. Different symbols can hinder with one another which results in noise and less relevant, reliable signal. Multipath propagation means a wireless signal from a spreader reaches the receiver through numerous paths. If inter symbol interfering occurs within an arrangement, it must be reduced to the most minimal quantity conceivable. The inter symbol interference is reduced through the use of adaptive algorithms; LMS (Least Mean Square Error) and RLS (Recursive Least Square) equalization procedures being the most prominent ones. In this work, a joint adaptive algorithm is proposed and simulated. A novel method involving both LMS and RLS is used for suppression of Inter symbol interference. The LMS has the benefit of a fast convergence rate but has a significant value of mean square error (MSE).However, the RLS complements it with low value of MSE and a slow convergence rate . Thus, a joint RLS and LMS is proposed The proposed method results in improved BER performance, lesser mean square error and a faster convergence.