Conventional Normalized Least Mean Square Research Articles

Single line noise cancellation using derivative of normalized least mean square algorithm

Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original error data normalized step size (EDNSS) algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.

Nonlinear adaptive normalized least mean absolute third algorithm for the control of five‐level distribution static compensator

SummaryThis paper presents the design and control of single‐phase five‐level cascaded H‐bridge multilevel inverter (CHB‐MLI) as a shunt active power filter (SAPF) unit to mitigate the various power quality (PQ) problems such as reactive power burden, injection of harmonics in the supply, poor power factor, and so on. The control of CHB‐MLI has been done using the normalized least mean absolute third (NLMAT) algorithm, which overcomes the shortcoming of conventional least mean square (LMS), normalized LMS (NLMS), and least mean absolute third (LMAT) algorithm. The control algorithm is designed in MATLAB Simulink, and its scaled down prototype model is developed in the laboratory to find the fundamental active component of load current and to mitigate the aforementioned PQ problems. The proposed system is tested for single‐phase grid‐connection, and it works satisfactorily under steady state and dynamic load conditions. A dSpace1104 microcontroller is used to test the proposed algorithm and to generate firing pulses. Besides, the detailed comparison in the context of mean square error (MSE), weights convergence and intermediate error with conventional LMS, NLMS, and LMAT algorithm have also been discussed. Moreover, the harmonic distortion observed in the source current is within stipulated IEEE‐519 limits.

Collaborative Blind Equalization for Time-Varying OFDM Applications Enabled by Normalized Least Mean and Recursive Square Methodologies

In this work, a collaborative blind equalization method for the orthogonal frequency division multiplexing (OFDM) signals in time-varying channels is presented. Equalizers are eliminated the inter-symbol interference (ISI) in the received signals caused by channel distortions. The conventional adaptive equalization requires sending the training-sequences periodically to synthesize the channel model, which can only provide redundant information, and consequently decrease the channel utilization and complicate the system. To overcome this drawback, the blind equalization methods, which need not send the training-sequences periodically, is developed. However, the conventional blind equalization methods still suffer from various disadvantages. The normalized least mean square (NLMS) method is able to converge rapidly, whereas its equalization error is relatively large. The recursive least square (RLS) method has smaller steady-state error but low convergence rate, demanding massive training sequences. To further enhance the equalization performance of time-varying OFDM systems, which is typically with massive calculation, a collaborative blind equalization is proposed in this work, which is able to effectively combine the characteristics of the conventional NLMS and RLS methods together. The numerical simulations demonstrate the proposed LM-RS method can exhibit quite good performance. Particularly, as compared with the conventional NLMS and RLS methods, the proposed LM-RS method achieves smaller steady-state error and lower complexity, as well as similar convergence rate. All these results indicate that the proposed collaborative LM-RS blind equalization method is suitable for the OFDM transmission under the time-varying wireless application environments.

Variable Step Size Normalized Least Mean Square Algorithm for Mobile Communication

This paper describes a robust variable step size normalized least mean square algorithm (VSSNLMSS) to enhance interference suppression in smart antenna system. The fixed step size normalized least mean square (NLMS) will result in a trade-off issue between convergence rate and steady-state mean square error (MSE) of NLMS algorithm. The purpose of a variable step-size normalized LMS algorithm is to solve the dilemma of fast convergence rate and low MSE. The proposed VSSNLMS algorithm reduces the MSE and shows faster convergence rate when compared to the conventional LMS and NLMS. Moreover, the enhanced performance of the new VSSNLMS algorithm is validated from the output beam pattern.

A Normalized Least Mean Square Algorithm Based on the Arctangent Cost Function Robust Against Impulsive Interference

In this paper, a normalized least mean square (NLMS) adaptive filtering algorithm based on the arctangent cost function that improves the robustness against impulsive interference is proposed. Owing to the excellent characteristics of the arctangent cost function, the adaptive update of the weight vector stops automatically in the presence of impulsive interference. Thus, this eliminates the likelihood of updating the weight vector based on wrong information resulting from the impulsive interference. When the priori error is small, the NLMS algorithm based on the arctangent cost function operates as the conventional NLMS algorithm. Simulation results show that the proposed algorithm can achieve better performance than the traditional NLMS algorithm, the normalized least logarithmic absolute difference algorithm and the normalized sign algorithm in system identification experiments that include impulsive interference and abrupt changes.

New Combined Adaptive (NCA) Algorithm for Smart Antenna System Applications

ABSTRACTThis paper proposes a new combined algorithm for performance enhancement of smart antenna systems in mobile communications. The new combined algorithm (NCA) uses sample matrix inversion (SMI) initialization with a modified version of normalized least mean square (NLMS) algorithm (MNLMS). The MNLMS algorithm is regarded as time-varying regularization parameter ϵ(k) that is fixed in the conventional NLMS algorithm. The MNLMS algorithm uses a reciprocal of the estimation error square term in the denominator of the learning rate of NLMS instead of fixed regularization parameter (ϵ). Through simulation results of smart antennas for an additive white Gaussian noise channel model, the NCA algorithm achieves about 15 and 19 dB improvement in interference suppression compared with NLMS and another proposed previously matrix inversion NLMS (MI-NLMS) algorithms, respectively. While the NLMS algorithm starts to converge from 50 iterations, the MI-NLMS and NCA algorithms start to converge from the initial iteration. Moreover, when this algorithm applied for the Rayleigh fading channel with a Jakes power spectral density, it provides about 7 dB improvements over NLMS and MI-NLMS algorithms and starts converge from the initial iteration. The NCA provides improved performance in weight stability for both channels.

BEAM STEERING IN SMART ANTENNAS BY USING LOW COMPLEX ADAPTIVE ALGORITHMS

Array antenna systems are often used to enhance the received signal to interference and noise ratio wh en the signal operates in heavily jammed environment. Proper modeling of the received data at different antenna elements is impo rtant when evaluating the performance of this system, especially when both th e signal and interference have wide frequency bands .. The antenna output is the linear combination of data from all the antenna ele ments. In conventional narrowband beam forming, tim e sequences at different antenna elements are related by some fixed phase sh ift. The phase shift is determined by the wave form s direction of arrival (DOA). In this paper, an efficient method for the pattern synthesis of the linear antenna arrays with the pre scribed nulling and steering lobe is presented. The proposed method is based on Least Me an Square (LMS) algorithm provide a comprehensive atreatment of the signal model used for beam forming, as well as, describing adaptive algorithms to adjust the weigh ts of an array. In order to improve the convergence rate of LMS algorithm in sm art antenna system, in this paper we proposes a ne w normalized LMS (NLMS) algorithm, This new algorithm can be treated as a b lock based simplification of NLMS algorithm which gsatisfactory performance in certain applications in comparison w ith conventional NLMS recursion, i.e., BBNLMS algor ithm. By taking advantage of spatial filtering, the proposed scheme promises to reduce the bandwidth required for transmitting d ata by improving convergence rate. The performance of the BBNLMS algorithm in th e presence of Multi-path effects and multiple users is analyzed using MATLAB simulations. The simulations when compared to that of the LMS algorithm, the results suggest that BB NLMS algorithm can improve the convergence rate and lead to better system effi ciency.

Derivation of a new normalized least mean squares algorithm with modified minimization criterion

In this letter, we propose a normalized least mean squares (NLMS) algorithm with a new minimization criterion that minimizes the summation of each squared Euclidean norm of difference between the currently updated coefficient vector and past coefficient vectors. Through simulations, we show that the proposed NLMS algorithm has lower misalignment than the conventional NLMS algorithm.

Comments on “Stereo echo cancellation algorithm using adaptive update on the basis of enhanced input-signal vector”

In the paper “Stereo echo cancellation algorithm using adaptive update on the basis of enhanced input-signal vector”, an enhanced form of the normalized least mean squares (NLMS) algorithm has been proposed. However, the enhanced algorithm has been derived by simply replacing the increment vector of the conventional NLMS with a new increment vector while not explicitly defining and validating the cost function of the algorithm. In this letter, we present the cost function of the algorithm, and prove that the algorithm is not a general LMS-type algorithm in some criterion.

Exponentially weighted stepsize NLMS adaptive filter based on the statistics of a room impulse response

A normalized least-mean-squares (NLMS) adaptive algorithm with double the convergence speed, at the same computational load, of the conventional NLMS for an acoustic echo canceller is proposed. This algorithm, called the ES (exponentially weighted stepsize) algorithm, uses a different stepsize (feedback constant) for each weight of an adaptive transversal filter. These stepsizes are time-invariant and weighted proportionally to the expected variation of a room impulse response. The algorithm adjusts coefficients with large errors in large steps, and coefficients with small errors in small steps. A transition formula is derived for the mean-squared coefficient error of the algorithm. The mean stepsize determines the convergence condition, the convergence speed, and the final excess mean-squared error. Modified for a practical multiple DSP structure, the algorithm requires only the same amount of computation as the conventional NLMS. The algorithm is implemented in a commercial acoustic echo canceller, and its fast convergence is demonstrated. >