scholarly journals Adaptive Equalizer Using Selective Partial Update Algorithm and Selective Regressor Affine Projection Algorithm over Shallow Water Acoustic Channels

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Masoumeh Soflaei ◽  
Paeiz Azmi
Keyword(s):  
Shallow Water ◽  
Water Channel ◽  
Lms Algorithm ◽  
Projection Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Equalizer ◽  
Affine Projection ◽  
The Family ◽  
Reliable Communications

One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI) which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA) and the family of selective partial update APA (SPU-APA) which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA and SPU-APA decrease by 5.8 (dB) and 5.5 (dB), respectively, in comparison with least mean square (LMS) algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm.

scholarly journals Multichannel Active Noise Control Based on Filtered-x Affine Projection-Like and LMS Algorithms with Switching Filter Selection

2019 ◽  
Vol 9 (21) ◽  
pp. 4669 ◽  
Author(s):  
Ángel A. Vázquez ◽  
Eduardo Pichardo ◽  
Juan G. Avalos ◽  
Giovanny Sánchez ◽  
Hugo M. Martínez ◽  
...  
Keyword(s):  
Steady State ◽  
Computational Cost ◽  
Lms Algorithm ◽  
Alternative Methods ◽  
Mean Square ◽  
Least Mean Square ◽  
Affine Projection ◽  
Mean Square Errors ◽  
Switching Filter ◽  
Filter Selection

Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

Convergence analysis of Bessel beamformer and its comparison with LMS in adaptive array system

2015 ◽  
Vol 34 (3) ◽  
pp. 952-961 ◽  
Author(s):  
M. Yasin ◽  
Pervez Akhtar
Keyword(s):  
Theoretical Model ◽  
Convergence Analysis ◽  
Digital Signal Processor ◽  
Wiener Filter ◽  
Digital Signal ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Content Type ◽  
Convergence Performance

Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

Hardware Implementation of Kalman Least Mean Square-Based Adaptive Algorithm for Denoising Ambient Noises in Shallow Water Region

2014 ◽  
Vol 13 (03) ◽  
pp. 1450018
Author(s):  
S. Sakthivel Murugan ◽  
V. Natarajan ◽  
S. Prethivika
Keyword(s):  
Shallow Water ◽  
Ambient Noise ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Useful Signal ◽  
Custom Made ◽  
Source Signal

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noise ratio (SNR) and mean square error (MSE) for various wind speeds. An exhaustive set of data, collected using a custom made fixture containing two hydrophones, from shallow water regions in Bay of Bengal, have been used to verify the efficacy of this method. Based on the results obtained by simulation and Lab window simulator, hardware has been designed to denoise the useful signal. The defective source signal is passed through a Kalman filter based denoising hardware system. This system performs necessary operations to denoise the defective source signal and the final turnout is made free from ambient noise. The denoised signal is then stored in an external device for future use.

scholarly journals SPEECH RECOGNITION USING SOM AND ACTUATION VIA NETWORK IN MATLAB

2014 ◽  
pp. 191-194
Author(s):  
A. SUBASH CHANDAR ◽  
S. SURIYANARAYANAN ◽  
M. MANIKANDAN
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Acoustic Noise ◽  
Lms Algorithm ◽  
Prototype System ◽  
Mean Square ◽  
Least Mean Square ◽  
Self Organizing Maps ◽  
Voice Signal ◽  
The Voice

This paper proposes a method of Speech recognition using Self Organizing Maps (SOM) and actuation through network in Matlab. The different words spoken by the user at client end are captured and filtered using Least Mean Square (LMS) algorithm to remove the acoustic noise. FFT is taken for the filtered voice signal. The voice spectrum is recognized using trained SOM and appropriate label is sent to server PC. The client and the server communication are established using User Datagram Protocol (UDP). Microcontroller (AT89S52) is used to control the speed of the actuator depending upon the input it receives from the client. Real-time working of the prototype system has been verified with successful speech recognition, transmission, reception and actuation via network.

scholarly journals Hybrid MVDR-LMS beamforming for Massive MIMO

2019 ◽  
Vol 16 (2) ◽  
pp. 715
Author(s):  
Yasmine M. Tabra ◽  
Bayan Sabbar
Keyword(s):  
Massive Mimo ◽  
High Speed ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Training Time ◽  
Multiple Users ◽  
Multiple Beams ◽  
Time Required ◽  
Beamforming Algorithm

<p>With the high speed of communication in LTE-5G, fast beamforming techniques need to be adopted. The training time required to form and steer the main lobes toward 5G multiple users must be short. Least-Mean-Square (LMS) training time is not suitable to work with in LTE-5G, but it has a good performance in forming multiple beams to large number of users and producing nulls in the interference direction. In this paper, an optimized hybrid MVDR-LMS beamforming algorithm is proposed to reduce the time required to estimate the antenna’s weights. This optimization is made by the benefit of previously set weights calculated using MVDR algorithms. The performance of the proposed hybrid MVDR-LMS algorithm tested using MATLAB 2016a.</p>

scholarly journals Adaptive Frequency domain Affine Projection Equalizer of MIMO SC-FDMA Based Overlap-Save Method

2021 ◽  
Author(s):  
Maha George
Keyword(s):  
Frequency Domain ◽  
Projection Algorithm ◽  
Adaptive Equalizer ◽  
Turbo Decoder ◽  
Affine Projection ◽  
Affine Projection Algorithm

Design adaptive equalizer using Affine projection algorithm for MIMO SC-FDMA and compare it with MMSE . Both equalizers are used within overlap-save method. Also a turbo decoder is used in conjunction with overlap-save method to enhance the BER performances in 8x8 and 16x16 MIMO SC-FDMA.

scholarly journals Performance Evaluation of OFDM System with Insufficient CP Using LMS Equalizer under Harsh Multipath Conditions

2019 ◽  
Vol 15 (2) ◽  
pp. 122-129
Author(s):  
Abolqassem Fakher ◽  
Falih Alnahwi ◽  
Majid Alwan
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
High Performance ◽  
Minimum Mean Square Error ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Ofdm System ◽  
Zero Forcing ◽  
Ofdm Signal ◽  
Multipath Signals

This paper presents an insufficient cyclic prefix (CP) Orthogonal Frequency Division Multiplexing (OFDM) system with equalizer whose coefficients are calculated using Least Mean Square (LMS) algorithm. The OFDM signal is passed through a channel with four multipath signals which cause the OFDM signal to be under high inter-symbol interference (ISI) and inter-carrier interference (ICI).8-QAM and 16-QAM digital modulation techniques are used to evaluate the performance of the proposed system. The simulation results have accentuated the high performance of the LMS equalizer via comparing its Bit Error Rate (BER) and constellation diagram with those of the Minimum Mean Square Error and Zero Forcing equalizers. Moreover, the results also reveal that the LMS equalizer provides BER performance close to that of the OFDM system with a hypothetical sufficient CP.

scholarly journals New feedforward filtered-x least mean square algorithm with variable step size for active vibration control

2018 ◽  
Vol 38 (1) ◽  
pp. 187-198 ◽  
Author(s):  
Yubin Fang ◽  
Xiaojin Zhu ◽  
Zhiyuan Gao ◽  
Jiaming Hu ◽  
Jian Wu
Keyword(s):  
Vibration Control ◽  
Active Vibration Control ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Large Step ◽  
Variable Step ◽  
Active Vibration

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

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