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Proceedings ArticleDOI

Analysis of minimum variance distortionless response and least mean square beamforming algorithm for smart antenna

TL;DR: Smart antenna attempt to enhance the received signal, suppress all interfering signals, and increase capacity, in result it increases capacity of mobile network.
Abstract: Smart antenna attempt to enhance the received signal, suppress all interfering signals, and increase capacity. Beamforming is one of the mostly used antenna technique. It adjust the radiation beam in one specific direction also reduces multiple access interference. It also reduces common channel interference and multiple path fading, in result it increases capacity of mobile network. In this paper two antenna beam forming algorithms are analyzed. These two algorithms are Least Mean Square and Minimum Variance Distortionless Response. Outputs of these two algorithms are in weights. By using these outputs weights, two parameters i.e. Mean Square Error and Power Beam Width are calculated and two algorithm are compared by this parameters and respective results are discussed.
Citations
More filters
Proceedings ArticleDOI
01 Nov 2019
TL;DR: The outcome demonstrates the data analysis approaches can provide better understanding to obtain the most optimum mother wavelet hence can enhance the performance of arc fault detection using wavelet transform as a denoising purposed.
Abstract: Arc fault detection in a power system network is a vital diagnostic test for condition monitoring in order to remain the consistency of the service. The issue of electrical faults has been largely investigated in the literature to foresee the existence of arc fault at the earliest stage. However, the identification measurement has faced noise disturbance from surrounding area. Thus, this paper presents an arc fault signals detection and analysis using Discrete Wavelet Transform (DWT) technique as a denoising technique. The effectiveness that influence the different types of mother wavelets is analyzed based on calculated signal to noise ratio (SNR) before denoising and after denoising, mean square error (MSE), correlation coefficient (CC) and mean absolute percentage error (MAPE). Each mother wavelet will be used to extract important features of a voltage signal from a single measurement point that were performed under 4th decomposition level using Universal (Sqtwolog) thresholding rule with soft thresholding function.The results are manipulated based on Haar (haar), Daubechies (db), Symlets (sym), Coiflets (coif), BiorSplines (bior) and Reverse-Bior (rbio) mother wavelets. The outcome demonstrates the data analysis approaches can provide better understanding to obtain the most optimum mother wavelet hence can enhance the performance of arc fault detection using wavelet transform as a denoising purposed.

4 citations


Cites background from "Analysis of minimum variance distor..."

  • ...The low value of ME indicates better performance of denoising technique [27]....

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Journal ArticleDOI
TL;DR: In this paper, a symmetric extension steering vector (SESV) is proposed to extend the dimensions of the covariance matrix of a real diagonal matrix to improve the robustness of the steering vector to transformation angle and interpolated step.
Abstract: Aiming at problems that interpolated array has large amount of computation and high sensitivity to transformation angle and interpolated step, a new array extension algorithm which is symmetric extension steering vector is proposed. In this paper, two properties of the conjugate of received data and the source covariance matrix being a real diagonal matrix are exploited to extend the dimensions of the covariance matrix. However, the essence of this extension method is the symmetric extension of the steering vector. The high complexity and degradation of the performance of interpolated array beamforming caused by the sensitivity of angle and interpolated step are improved. Numerical simulations confirm the validity of the proposed algorithm. Compared with existing algorithms, the proposed algorithm is not affected by the angle range of transformation and interpolated step. Besides, the complexity of array extension using this proposed algorithm is much lower than the interpolated transformation method.

1 citations


Cites methods from "Analysis of minimum variance distor..."

  • ...In this paper, the proposed array extension technique is combined with MVDR....

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  • ...Beamforming Based on Steering Vector Conjugate Symmetry Array The main beamforming algorithms include Least Mean Square (LMS) [18], Sample Matrix Inversion (SMI) [19], and Minimum Variance Distortionless Response (MVDR) [18, 20]....

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  • ...The main beamforming algorithms include Least Mean Square (LMS) [18], Sample Matrix Inversion (SMI) [19], and Minimum Variance Distortionless Response (MVDR) [18, 20]....

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  • ...(12) can be simplified as: Z ( UnΛnUHn ) ZH = σ2nI (12) Solving the above formula and getting the whitening matrix is: Z = ( σ2n )1/2 Λ−1/2n UHn (13) So the whitened covariance matrix can be described as: ˜̄R = Ā [ RsA T A∗Rs RsA HA∗Rs RsA T ARs RsA HARs ] ĀH + ZR̄nZH (14) Combined with the MVDR algorithm, the optimal weight can be obtained by: ˜̄wopt = ˜̄R−1ā (θ0) āH (θ0) ˜̄R−1ā (θ0) (15) where ā (θ0) is an extended steering vector of the desired direction....

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References
More filters
Journal ArticleDOI
TL;DR: An overview of beamforming from a signal-processing perspective is provided, with an emphasis on recent research.
Abstract: An overview of beamforming from a signal-processing perspective is provided, with an emphasis on recent research. Data-independent, statistically optimum, adaptive, and partially adaptive beamforming are discussed. Basic notation, terminology, and concepts are included. Several beamformer implementations are briefly described. >

4,122 citations

Book
31 May 1997
TL;DR: Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.
Abstract: From the Publisher: Adaptive Filtering: Algorithms and Practical Implementation is a concise presentation of adaptive filtering, covering as many algorithms as possible while avoiding adapting notations and derivations related to the different algorithms. Furthermore, the book points out the algorithms which really work in a finite-precision implementation, and provides easy access to the working algorithms for the practicing engineer. Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.

1,294 citations


"Analysis of minimum variance distor..." refers background in this paper

  • ...H is the hermitian matrix and a( ) is the steering vector which is given by; (2) Where d is the space between the elements of the antenna, is the desired angle, and N is the number of elements [9] [11] [12] [13]....

    [...]

Journal ArticleDOI
01 Jul 1997
TL;DR: This paper presents an overview of mobile communications as well as details of how an array may be used in various mobile communications systems, including land-mobile, indoor-radio, and satellite-based systems.
Abstract: The demand for wireless mobile communications services is growing at an explosive rate, with the anticipation that communication to a mobile device anywhere on the globe at all times will be available in the near future. An array of antennas mounted on vehicles, ships, aircraft, satellites, and base stations is expected to play an important role in fulfilling the increased demand of channel requirement for these services, as well as for the realization of the dream that a portable communications device the size of a wristwatch be available at an affordable cost for such services. This paper is the first of a two-part study. It provides a comprehensive treatment, at a level appropriate to nonspecialists, of the use of an antenna array to enhance the efficiency of mobile communications systems. It presents an overview of mobile communications as well as details of how an array may be used in various mobile communications systems, including land-mobile, indoor-radio, and satellite-based systems. It discusses advantages of an array of antennas in a mobile communications system, highlights improvements that are possible by using multiple antennas compared to a single antenna in a system, and provides details on the feasibility of antenna arrays for mobile communications applications.

1,052 citations


"Analysis of minimum variance distor..." refers background in this paper

  • ...H is the hermitian matrix and a( ) is the steering vector which is given by; (2) Where d is the space between the elements of the antenna, is the desired angle, and N is the number of elements [9] [11] [12] [13]....

    [...]

Journal ArticleDOI
TL;DR: Simulation results suggest that coordinating the beamforming vectors alone already provide appreciable performance improvements as compared to the conventional per-cell optimized network.
Abstract: In a conventional wireless cellular system, signal processing is performed on a per-cell basis; out-of-cell interference is treated as background noise. This paper considers the benefit of coordinating base-stations across multiple cells in a multi-antenna beamforming system, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. Consider a multicell downlink scenario where base-stations are equipped with multiple transmit antennas employing either linear beamforming or nonlinear dirty-paper coding, and where remote users are equipped with a single antenna each, but where multiple remote users may be active simultaneously in each cell. This paper focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of the paper is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provide appreciable performance improvements as compared to the conventional per-cell optimized network.

719 citations

Journal ArticleDOI
TL;DR: A selective-partial-update normalized least-mean-square (NLMS) algorithm is developed, and its stability is analyzed using the traditional independence assumptions and error-energy bounds, and the new algorithms appear to have good convergence performance.
Abstract: In some applications of adaptive filtering such as active noise reduction, and network and acoustic echo cancellation, the adaptive filter may be required to have a large number of coefficients in order to model the unknown physical medium with sufficient accuracy. The computational complexity of adaptation algorithms is proportional to the number of filter coefficients. This implies that, for long adaptive filters, the adaptation task can become prohibitively expensive, ruling out cost-effective implementation on digital signal processors. The purpose of partial coefficient updates is to reduce the computational complexity of an adaptive filter by adapting a block of the filter coefficients rather than the entire filter at every iteration. In this paper, we develop a selective-partial-update normalized least-mean-square (NLMS) algorithm, and analyze its stability using the traditional independence assumptions and error-energy bounds. Selective partial updating is also extended to the affine projection (AP) algorithm by introducing multiple constraints. The new algorithms appear to have good convergence performance as attested to by computer simulations with real speech signals.

196 citations


"Analysis of minimum variance distor..." refers background in this paper

  • ...H is the hermitian matrix and a( ) is the steering vector which is given by; (2) Where d is the space between the elements of the antenna, is the desired angle, and N is the number of elements [9] [11] [12] [13]....

    [...]