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This paper proposed a novel design for a high-speed adaptive filter, specifically a new filter structure employing a convex combination architecture incorporating systolic architecture. The performance of convex architecture and systolic architecture are analyzed for LMS and RLS filters in two different orders. Systolic architecture improves the speed of convergence and SNR of the filters. Additionally convex combination architecture significantly improves the overall filter performance, especially in steady-state conditions. The combination of these two filter design techniques results in significantly improved convergence speed and steady-state performance of filters. The performance of the proposed filters combined two architectures outperforms the conventional filters in several metrics for filter design, including convergence speed, SNR, Steady-state MSE, learning capability, and filtering capability in terms of residual noise. Simulation results demonstrate the effectiveness of proposed filters in denoising ECG signals from PLI noise. The suggested filter configurations undergo evaluation for the purpose of ECG noise elimination for a set of ten ECG signals, acquired from the MIT-BIH database. [ABSTRACT FROM AUTHOR]

In this paper, an improved high-speed adaptive filter is proposed and implemented using a field-programmable gate array platform. Specifically, a new filter structure combining systolic and convex architectures has been analyzed and compared with conventional filter architectures. The new filter structure efficiently removes power line interference noise from electrocardiogram signals at high convergence speeds. The systolic architecture is used to improve the convergence speed of the filter, and the convex architecture is used in combination to improve the signal-to-noise ratio of the filter. One fast filter was designed using the retimed delay recursive least square algorithm (recursive least square filter design with systolic architecture), and it was combined with one slow filter designed using the least mean square algorithm based on convex combination architecture. The proposed filter architectures are assessed for electrocardiogram noise cancellation, obtained from the MIT-BIH database, and the performance is compared with various filter structures in terms of the signal-to-noise ratio, convergence speed, learning behaviors, and complexity. The results show an improvement in signal-to-noise ratio of 24.2% and an increase in convergence speed of 50% when compared with conventional filter structures. [ABSTRACT FROM AUTHOR]

This study explores the application of LMS and RLS algorithms in adaptive beamforming for a linear array of half wavelength dipole (HWD) antennas with known mutual coupling. The research initiates by deriving analytical expressions for the covariance matrix of signal impinging on an HWD antenna array, accounting for mutual coupling effects. Leveraging these expressions, we calculate the weights for both LMS and RLS algorithms, enabling the evaluation of the overall far-field radiation patterns of the HWD antenna array. The results demonstrate that these algorithms effectively steer the main lobe of the radiation pattern towards the desired user while creating nulls in the direction of undesired users. Notably, simulations indicate that the proposed compensation method enhances the performance of beamforming algorithms, particularly when considering mutual coupling effects. Specifically, the LMS algorithm outperforms the RLS algorithm in reducing interference, resulting in lower side lobe levels (SLL) in the radiation pattern. [ABSTRACT FROM AUTHOR]

This paper proposes an improved Recursive Least Square (RLS) algorithm to address the limitations of the conventional RLS algorithm, which suffers from poor performance when dealing with non-stationary noise. The proposed algorithm, along with various algorithms, have been designed using Xilinx System Generator (XSG). These algorithms have been tested to denoise ECG signals that have been corrupted with four types of real noise obtained from the MIT-BIH dataset. The improved algorithm utilizes a systolic architecture that enables faster processing and better noise reduction capabilities. The experimental results demonstrate that the proposed algorithm with and without systolic architecture outperforms the conventional RLS algorithm and other state-of-the-art algorithms in terms of signal-to-noise ratio (SNR), mean squared error (MSE), and convergence speed.

In this paper, we study the problem of beam tracking of a multipath channel in millimeter-wave massive MIMO communication system using adaptive filters. We focus on the performance of least-mean-square filter (LMS) and recursive least-squares filter (RLS) algorithms, compared to a reference extended Kalman filter (EKF), in scenarios where the wireless channel is dominated by a single line of sight (LOS) path or a small number of strong paths. The signal direction and channel coefficients are tracked and updated using these filters. Our results recommend that beamforming systems at millimeter-wave bands should consider variable number of paths rather than a single dominant LOS path. Furthermore, we show that the mean squared-error (MSE) of the innovation process gives a better overall view of the tracking performance than the MSE of the state parameters.

In this paper, we study the problem of beam tracking of a multipath channel in millimeter-wave massive MIMO communication system using adaptive filters. We focus on the performance of least-mean-square filter (LMS) and recursive least-squares filter (RLS) algorithms, compared to a reference extended Kalman filter (EKF), in scenarios where the wireless channel is dominated by a single line of sight (LOS) path or a small number of strong paths. The signal direction and channel coefficients are tracked and updated using these filters. Our results recommend that beamforming systems at millimeter-wave bands should consider variable number of paths rather than a single dominant LOS path. Furthermore, we show that the mean squared-error (MSE) of the innovation process gives a better overall view of the tracking performance than the MSE of the state parameters. [ABSTRACT FROM AUTHOR]

در سیستم‌های MIMO روش‌های متنوعی برای تخمین کانال وجود دارد که هر کدام نیز دارای عملکردهای متفاوتی هستند. سیستم‌های MIMO مانند یک دستگاه چند معادله و چند مجهول هستند که مجهولات، M سمبل ارسالی از فرستنده و معادلات، N سمبل دریافتی در گیرنده هستند، از این رو از روش‌های مختلف تجزیه ریاضی برای تجزیه ماتریس کانال استفاده می‌شود که تجزیه QRD یکی از این روش‌ها می‌باشد. استفاده از تجزیه ماتریس کانال به همراه الگوریتم‌های وفقی مانند LMS و RLS برای سیستم‌های MIMO سادگی و کاهش پیچیدگی را در پی خواهد داشت. در این مقاله با استفاده از دو الگوریتم LMS و RLS کانال MIMO مدل خواهد شد و به جای تخمین مستقیم ماتریس H کانال که پیچیدگی را به همراه دارد، ابتدا Q و بعد R از ماتریس QR تخمین زده می‌شود که همان تخمین ماتریس کانال می‌باشد. نتایج نشان می‌دهد که مدل کردن کانال با الگوریتم‌های وفقی LMS و RLS و استفاده از تجزیه ریاضی QR کاهش خطا در سیستم‌های MIMO را بدست می‌دهد.

Smart antennas are becoming popular in the area of cellular wireless communication for capacity enhancement and reduction of the multipath effect and interference. The demand for smart antennas is widely increasing as 5G cellular communication evolves to support the higher data rate and bandwidth. The basic principle of smart antenna design is adaptive beamforming using the best suited digital signal processing algorithms, such as least mean square (LMS), normalized least mean square (NLMS), sample matrix inversion (SMI), and recursive least square (RLS), each having its pros and cons. Among these, the LMS and NLMS are iterative approaches while SMI is a block adaptive method and RLS is a recursive method. The contribution of this article includes easy implementation of four adaptive beamforming algorithms, namely LMS, NLMS, SMI, and RLS. Furthermore, an exhaustive comparative performance analysis is carried out under five interferers and evaluated in terms of beamwidth, null depth, maximum sidelobe level, rate of convergence, error variation for the number of antenna elements, and spacing. Finally, a contrast table is presented to demonstrate the pros and cons of the listed algorithms. Time and dynamic space complexity are studied for RLS and SMI beamforming algorithms. Performance results from a designed reconfigurable testbed model for weight adaptation and smart beamforming are also presented for analysis and a better understanding of the real implementation of the smart algorithms through testbed design. The simulation and testbed results create the ground work for future exploration in the design of smart antenna beamforming. [ABSTRACT FROM AUTHOR]

In this work, we propose a fast conjugate gradient method (CGM) for beamforming, after thoroughly analyzing the performances of the least mean square (LMS), the recursive least square (RLS), and the sample matrix inversion (SMI) adaptive beamforming algorithms. Various experiments are carried out to analyze the performances of each beamformer in detail. The proposed conjugate gradient method does not use the Eigen spread of the signal correlation matrix as in the case of the LMS and the RLS methods. It computes antenna array weights orthogonally for each iteration. Hence the convergence rate and the null depths of the proposed method are much better than the LMS, the SMI the RLS and the classical CGM. Also, the simulation results confirm that this method has a speed improvement of about 60% over the classical conjugate gradient method. This aspect significantly reduces the processor burden and saves a lot of power during the beamforming process. Hence the proposed method is superior compared to the LMS, the RLS, the SMI, and classical CGM and most suitable for high-speed mobile communication. [ABSTRACT FROM AUTHOR]

Digital Filter design plays a vital role in signal processing and communication applications. This paper proposes a hearing loss system model with Variable Bandwidth FIR Filter (VBF) and adaptive algorithms for the application to listening. The tunable band filter is designed to provide an appropriate sound level. This filter has several sub-filters each of which is designed with set of selected bandwidths. The sub-bands obtained are adjusted with proper magnitude by trial and error method. Algorithms such as Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) are incorporated to improve the quality of the signal. The filter thus designed is examined by taking a number of audio signals. The tests on various hearing loss cases with different type of input signal suggest that this method is capable of reproducing a signal which sounds exactly the same as the original signal. The multiple bandwidth filters is analyzed with mild, moderate, profound and severe hearing loss patterns and the results are reported. The matching error is calculated between ideal response and actual response. The result show that the designed filter provides acceptable minimum matching error and it lies in the range 0 to 2.5dB.This filter design is implemented in TMS320C6711 processor and is tested for sinusoidal input signal. [ABSTRACT FROM AUTHOR]

In recent literature, several techniques have been adopted to reject noise from EMG recordings, which is important for using EMG techniques in clinics and research work. In this paper, the least mean square (LMS) and recursive least square (RLS) adaptive algorithms have been proposed to denoise uterine EMG signals taken from the open-source database TPEHG. Simulations were performed in the MATLAB Simulink environment. The obtained results were then compared with the wavelet transform and 0.34–1.0 Hz bandpass filter using signal-to-noise ratio measures. The results showed that the RLS-based algorithm performs the best among the other tested algorithms; the possible use of uterine EMG signals for prognostication of premature vs. term delivery has been taken into consideration. [ABSTRACT FROM AUTHOR]

A review of noise cancellation in communication systems based on adaptive filter algorithms is presented. The modifying of signal characteristics in noise cancellation is relatively fast which involves the utilization of adaptive algorithms for quick coverage. The LMS and RLS algorithms play a vital role in noise cancellation to increase the convergence rate. Current communication applications require reducing the computational complexity in real-time implementation. The category of adaptive filters is automatically changing the parameters of its algorithms according to the input signal. Many adaptive filters are digitally executed to update the algorithm parameters. The original applications of an adaptive filter in numerous fields including system recognition and noise cancellation can be modeled by MATLAB simulator. The input signal of an adaptive filter is with the mixed algorithms and noise signal in the specific design of MATLAB to remove the noise from the original data and produce reliable data at the output port. The filter implementation shows the strength of the suggested structure and the capabilities of the proposed algorithms have been investigated and analyzed. The performance of the filter is promising to support the current and future communication systems in terms of noise cancellation. [ABSTRACT FROM AUTHOR]

Advanced Communication Systems are wideband systems to support multiple applications such as audio, video and data so and so forth. These systems require high spectral efficiency and data rates. In addition, they should provide multipath fading and inter-symbol interference (ISI) free transmission. Multiple input multiple output orthogonal frequency division multiplexing (MIMO OFDM) meets these requirements Hence, MIMOOFDM is the most preferable technique for long term evaluation advanced (LTE-A). The primary objective of this paper is to control bit error rate (BER) by proper channel coding, pilot carriers, adaptive filter channel estimation schemes and space time coding (STC). A combination of any of these schemes results in better BER performance over individual schemes. System performance is analyzed for various digital modulation schemes. In this paper, adaptive filter channel estimated MIMO OFDM system is proposed by integrating channel coding, adaptive filter channel estimation, digital modulation and space time coding. From the simulation results, channel estimated 2×2 MIMO OFDM system shows superior performance over individual schemes. [ABSTRACT FROM AUTHOR]

Equalization is a most widely used optimization scheme to reduce inter-symbol interference (ISI).Intersymbol interfering environment is a case of misrepresentation of the sent signal at the receiver. Different symbols can hinder with one another which results innoise 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. [ABSTRACT FROM AUTHOR]

Signal de-noising has been sparked and given a great attention by signal processing community since its applications are found in a diverse range of digital signal processing and computer vision problems. To improve signal quality, phase, frequency and power are the important features that should be preserved during the de-noising process. Adaptive filters have been widely used for this purpose due to their ability to cancel out noise signal from the corrupted one precisely. This paper presents a robust adaptive estimator for solving the problem of signal noise cancellation, based on a new adaptive algorithm derived from a new constrained optimization. Simulation results evaluated using MATLAB show that the proposed algorithm is appropriate for several forms of signals contaminated by diverse levels of noise power. The performance of the proposed algorithm is illustrated to be preferable in terms of the power signal to noise ratio, mean square error and time of speed convergence of filter parameters. It is compared to other conventional approaches such as least mean square and normalized least mean square algorithms with various values of white noise power, variance. It exhibits lower steady-state error and faster convergent time than the other implementations. Finally, an efficient performance is achieved comparable with recursive least square and affine projection algorithms. [ABSTRACT FROM AUTHOR]

This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted. [ABSTRACT FROM AUTHOR]

While inter-symbol interference is not of concern, inter-carrier interference that is caused by carrier frequency offset (CFO) is more concerned in orthogonal frequency division multiplexing (OFDM) systems. The estimation of channel impulse response (CIR) is another important parameter of interest in the system. Recently, a number of approaches for the joint estimation of CFO and CIR in OFDM systems have been proposed in the literature, and most of them are traditional maximum-likelihood-based approaches. In this paper, we propose a number of mixture cost-reference particle filtering (CRPF) approaches for joint estimation of CIR and CFO, and a couple of them can be applied without the knowledge of both state and measurement noise statistics. Conventional least mean squares algorithm or recursive least squares algorithm is jointly employed with recently developed CRPF for nonlinear and multiple parameters estimation in OFDM systems. The computer simulation results show that mixture CRPFs are more robust against particle impoverishment (PI) problem than standard mixture particle filtering (PF) and mixture regularized PF (RPF) approaches although RPF was particularly developed to tackle the PI problem. In this paper, we investigate a number of mixture PF approaches including ones that do not need the information of noise statistics for the joint estimation of CFO and channel in OFDM systems. [ABSTRACT FROM PUBLISHER]

In this paper, a variable step size based least mean squares (LMS) channel estimation (CE) algorithm is presented for a single carrier frequency division multiple access(SC-FDMA) system under the umbrella of the long term evolution (LTE). This unbiased CE method can automatically adapts the weighting coefficients on the channel condition. Therefore, it does not require knowledge of channel,and noise statistics. Furthermore, it uses a phase weighting scheme to eliminate the signal fluctuations due to noise and decision errors. Such approaches can guarantee the convergence towards the true channel coefficient. The mean and mean square behaviors of the proposed CE algorithm are also analyzed. With the help of theoretical analysis and simulation results, we prove that the proposed algorithm outperforms the existing algorithms in terms of mean square error (MSE) and bit error rate (BER) by more than around 2.5dB.

En este trabajo se describen brevemente los sistemas de antenas inteligentes. Se presentan algunos algoritmos adaptativos que hacen que el sistema sea inteligente. Los algoritmos que se presentan son LMS y el RLS y serán sometidos a un análisis matemático para su posterior simulación vía MatLab. Éstos se escogen por su relación con un algoritmo híbrido desarrollado. Cada algoritmo será evaluado respecto a diferentes parámetros tales como convergencia, rapidez, estabilidad, etcThis paper briefly describes intelligent antenna systems. Some adaptive algorithms that render the system intelligent are presented. LMS and RLS algorithms are introduced and subject to mathematical analysis in order to allow simulation by using MatLab. The algorithms are chosen according to their relationship with the hybrid algorithm developed by the authors. Each algorithm is evaluated with reference to parameters such as convergence, speed, stability, etc

This paper analyses the performance of Adaptive Channel Equalizer when designed using different algorithms. Adaptive equalization in digital communication is a process of compensating the disruptive effects caused mainly by intersymbol interference in a band limited channel and plays a vital role for enabling higher data rate in modern digital communication systems. [ABSTRACT FROM AUTHOR]

The main objective of this study is to compensate channel distortions in GPS-Aided GEO Augmented Navigation (GAGAN) applications by means of adaptive equalization. Positional accuracy of GAGAN system is basically dependent on ranging errors and satellite constellation geometry. Further, this study focuses on enhancing the phenomena ofinstrumental biases and GAGAN augmentation. [ABSTRACT FROM AUTHOR]

In this paper, a comparative study between a blind algorithm, based on higher order cumulants, and adaptive algorithms, i.e. Recursive Least Squares (RLS) and Least Mean Squares (LMS) for MultiCarrier Code Division Multiple Access (MC-CDMA) systems equalization is presented. Two practical frequency-selective fading channels, called Broadband Radio Access Network (BRAN A, BRAN B) normalized for MC-CDMA systems are considered. In the part of MC-CDMA equalization, the Zero Forcing (ZF) and the Minimum Mean Square Error (MMSE) equalizer techniques were used. The simulation results in noisy environment and for different signal to noise ratio (SNR) demonstrate that the blind algorithm gives approximately the same results obtained by adaptive algorithms. However, the proposed algorithm presents the advantage to estimate the impulse response of these channels blindly except that the input excitation is non-Gaussian, with the low calculation cost, compared with the adaptive algorithms exploiting the information of input and output for the impulse response channel estimation.

The main interest in adaptive filters continues to grow as they begin to find practical applications in areas such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal-processing applications. The work presented in this paper focuses on optimizing most popular adaptive filtering algorithms namely Least Mean Square (LMS) algorithm, Normalized Least Mean Square (NLMS) and Rec ursive least Squares (RLS) by using genetic optimizer approach. The tap-length are updated with the three adaptive algorithms according to the value of mean square error based on genetic style. The simulation results for noise cancellation in speech enhancement demonstrate the good performance of the proposed algorithm in attenuating the noise with less hardware resources complexity. It is a nice tradeoff between hardware complexity, SNR ratio and the convergence speed. [ABSTRACT FROM AUTHOR]