In Massive multiple -input multiple -output (MIMO) systems, channel estimation is crucial. The large size of the antennas causes a significant pilot and feedback overhead, making it challenging to estimate channels in...
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In Massive multiple -input multiple -output (MIMO) systems, channel estimation is crucial. The large size of the antennas causes a significant pilot and feedback overhead, making it challenging to estimate channels in massive MIMO systems. Besides, the studies have shown that mmWave channels are sparse due to the limited number of dominant propagation paths. Therefore, the motivation of this paper is to exploit the inherent sparsity of the massive mmWave MIMO channels to develop a semi -blind channel estimation with reduced number of pilots. To this goal, an expectation maximization (EM) based technique has been developed which leverages the sparsity of the underlying channel for its better estimation. An iterative approach is proposed to solve the modeled problem which simultaneously updates the channel coefficients and data symbols using available data and system structure at each iteration. The proposed method imposes sparsity with the aid of Smoothed L 0 norm (sl0) in the M -step. The simulation results demonstrate the proposed method have quick convergence and lower channel estimation error compared to the existing methods. As a quantitative evaluation, the proposed method attains the normalized mean square error of 9 x 10 -2 and 7 x 10 -3 at SNR = 5 dB and SNR = 15 dB, respectively.
The sl0 algorithm is a sort of sparse reconstruction algorithm approximate to l0 norm, which has significant applications in the field of deblurring. In the sl0 algorithm, usually a number of important parameters need...
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ISBN:
(纸本)9781510639560
The sl0 algorithm is a sort of sparse reconstruction algorithm approximate to l0 norm, which has significant applications in the field of deblurring. In the sl0 algorithm, usually a number of important parameters need to be set to obtain deblurred images. This paper first introduces the basic of the sl0 algorithm, then it analyzes the operator, which has higher dipartite degree for blurred images in edge extraction algorithm, and choose the Roberts operator as the standard for judging parameter optimization. Finally, an algorithm for image restoration parameter adaptive selection is designed, and experiments are conducted. The experimental results show that comparing with the traditional sl0 algorithm, the algorithm in this paper has a great improvement in terms of repairing quality. The repairing effect of the algorithm in this paper is more natural, and the PSNR of images can be increased about 1.5dB.
To enable the smoothed l(0)-norm (sl0) algorithm to offer accurate estimates of the target parameters in three dimensions of range, angle, and Doppler, the fine discretisation of the potential target space is required...
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To enable the smoothed l(0)-norm (sl0) algorithm to offer accurate estimates of the target parameters in three dimensions of range, angle, and Doppler, the fine discretisation of the potential target space is required for multiple-input multiple-output (MIMO) radars, which results in the ill-conditioned sensing matrix. Unfortunately, the sl0 algorithm will provide unacceptable results since the large errors occur in computing initial value and performing projection onto the feasible set through the use of the pseudo-inverse of the ill-conditioned sensing matrix. In this study, the authors present a robust sl0 approach for MIMO radars to provide accurate angle-range-Doppler estimates. The appropriate permutation matrix, which takes the place of the pseudo-inverse of the sensing matrix in implementing sl0 algorithm, can be pre-computed by taking advantage of the bi-conjugate gradient stabilised approach and the singular value decomposition (SVD) of the sensing matrix. Simulation results show that the proposed algorithm not only has lower computational cost, but provides better performance in estimation of range-angle-Doppler parameters, compared with the regularised iterative reweighted minimisation approach, sl0 algorithm and its modified versions in combination with Tikhonov and truncated SVD methods.
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