• The Journal of the Acoustical Society of Korea
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• v.31 no.2
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• pp.79-86
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• 2012

In this paper, we propose an enhanced algorithm for affine projection algorithms which have been proposed to speed up the convergence of the conventional NLMS algorithm. Since affine projection (AP) or pseudo AP algorithms are based on the delayed input vector and error vector, they are complicated and not suitable for applying methods developed for the LMS-type algorithms which are based on the scalar error signal. We devised a variable step size algorithm for pseudo AP using the fact that pseudo AP algorithms are updated using the scalar error and that the error signal is getting orthogonal to the input signal. We carried out a performance comparison of the proposed algorithm with other pseudo AP algorithms using a system identification model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments despites its low complexity.

• Smart Media Journal
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• v.8 no.1
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• pp.43-50
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• 2019

As numerous devices are connected through a wireless network, there exist many studies conducted to efficiently connect the devices. While earlier studies often use clustering for efficient device management, there is a load-intensive cluster node which may lead the entire network to be unstable. In order to solve this problem, we propose a scheduling model for centralized unequal chain clustering for efficient management of sensor nodes. For the cluster configuration, this study is based on the cluster head range and the distance to the base station(BS). The main vector projection technique is used to construct clustering with concentricity where the positions of the base stations are not the same. We utilize a multiple radio access interface, multiple-input multiple-output (MIMO), for data transmission. Experiments show that cluster head energy consumption is reduced and network lifetime is improved.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.148-156
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• 2014

In the MVDR (minimum variance distortionless response) adaptive array, its performance could be greatly deteriorated in the presence of steering vector errors as the desired signal is treated as an interference. This paper suggests an computationally simple adaptive beamforming method which is robust against these errors. In the proposed method, a minimization problem that is formulated according to the DCB (doubly constrained beamforming) principle is solved to find a solution vector, which is in turn projected onto a subspace to obtain a new steering vector. The minimization problem and the subspace projection are dealt with using some principal eigenpairs, which are obtained using a modified PASTd(projection approximation subspace tracking with deflation). We improve the existing MPASTd(modified PASTd) algorithm such that the computational complexity is reduced. The proposed beamforming method can significantly reduce the complexity as compared with the conventional ones directly eigendecomposing an estimate of the corelation matrix to find all eigenvalues and eigenvectors. Moreover, the proposed method is shown, through simulation, to provide performance improvement over the conventional ones.