Performance Analysis of LMS Adaptive Beamforming Algorithm for Smart Antenna System

Pandhare, Ashwini; Zalawadia, Khyati R.

doi:10.5120/ijca2018916633

IJCA

2018

DOI: 10.5120/ijca2018916633

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Performance Analysis of LMS Adaptive Beamforming Algorithm for Smart Antenna System

Ashwini Pandhare¹,

Khyati R. Zalawadia²

Abstract: As the growing need for wireless communications is constantly increasing, the demand for better coverage, enhance user capacity, and higher transmission quality rises. To provide interference mitigation and enhance user capacity the smart antenna system is one of the promising technologies. This paper presents performance analysis of LMS (Least Mean Square) adaptive beamforming algorithm for smart antenna system. Use different spacing between array element, increase number of array elements and also use differ… Show more

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Cited by 4 publications

References 6 publications

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Signal Sub-Space Based Variable Step Size LMS Adaptive Beamforming Algorithm for Massive MIMO Systems

慈¹

2018

HJWC

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In this paper, an improved variable step size LMS adaptive beamforming algorithm based on signal sub-space (SS-VSLMS) is proposed to solve some problems of LMS adaptive beamforming algorithm when the eigenvalue of the correlation matrix is not ideal for large scale MIMO systems. The weight vector in the algorithm only preserves the subspace component of the signal, and can obtain the optimal step length based on the exponential factor and improve the convergence speed of the algorithm. At the same time, the error correlation analysis is introduced to minimize the steady state error, and the power vector of the signal subspace is extracted to improve the anti-interference ability of the system. Simulation results show that the improved algorithm can meet the convergence and anti-interference requirements of large-scale MIMO systems.

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Signal Sub-Space Based Variable Step Size LMS Adaptive Beamforming Algorithm for Massive MIMO Systems

慈¹

2018

HJWC

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An Anti-Main-Lobe Jamming Algorithm for Airborne Early Warning Radar based on APC-SVRGD Joint Optimization

Fang

Wang

et al. 2022

J. of Syst. Eng. Electron.

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Main lobe jamming seriously affects the detection performance of airborne early warning radar. The joint processing of polarization-space has become an effective way to suppress the main lobe jamming. To avoid the main beam distortion and wave crest migration caused by the main lobe jamming in adaptive beamforming, a joint optimization algorithm based on adaptive polarization canceller (APC) and stochastic variance reduction gradient descent (SVRGD) is proposed. First, the polarization plane array structure and receiving signal model based on primary and auxiliary array cancellation are established, and an APC iterative algorithm model is constructed to calculate the optimal weight vector of the auxiliary channel. Second, based on the stochastic gradient descent principle, the variance reduction method is introduced to modify the gradient through internal and external iteration to reduce the variance of the stochastic gradient estimation, the airspace optimal weight vector is calculated and the equivalent weight vector is introduced to measure the beamforming effect. Third, by setting up a planar polarization array simulation scene, the performance of the algorithm against the interference of the main lobe and the side lobe is analyzed, and the effectiveness of the algorithm is verified under the condition of short snapshot number and certain signal to interference plus noise ratio.

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Improved NLMS-based adaptive denoising method for ECG signals

Wang

Liu

et al. 2021

THC

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BACKGROUND: Traditional least mean square algorithm (LMS) tends to converge faster and thus the larger the steady-state error of the algorithm. OBJECTIVE: In order to solve this issue, an improved adaptive normalized least mean square (NLMS) ECG signal denoising algorithm is proposed through utilizing the NLMS and the least mean square algorithm with added momentum term (MLMS). METHODS: The algorithm firstly performs LMS adaptive filtering on the original ECG signal. Then, the algorithm uses the relative error of the prior error signal and the posterior error signal before and after filtering to adaptively determine the iteration step factor. Finally, the expected error is set to determine whether the denoising meets the expected requirements. This method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology. RESULTS: Experimental results have shown that the proposed algorithm can achieve good denoising for the target signal, and the average signal to noise ratio (SNR) of the proposed method is 17.6016, the RMSE is only 0.0334, and the average smoothness index R is only 0.0325. CONCLUSION: The proposed algorithm effectively removes the original ECG signal noise, and improves the smoothness of the signal the denoising efficiency.

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Performance Analysis of LMS Adaptive Beamforming Algorithm for Smart Antenna System

Cited by 4 publications

References 6 publications

Signal Sub-Space Based Variable Step Size LMS Adaptive Beamforming Algorithm for Massive MIMO Systems

Signal Sub-Space Based Variable Step Size LMS Adaptive Beamforming Algorithm for Massive MIMO Systems

An Anti-Main-Lobe Jamming Algorithm for Airborne Early Warning Radar based on APC-SVRGD Joint Optimization

Improved NLMS-based adaptive denoising method for ECG signals

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