Sparse channel estimation using orthogonal matching Pursuit algorithm for SCM-OFDM system

Rashmi, N; Sarvagya, Mrinal

doi:10.1109/icacci.2016.7732212

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Many ways have been put forward to solve this problem, such as the tracking matching algorithm and the greedy algorithm. Especially, there is an orthogonal matching pursuit algorithm that estimates sparse signal channels by reducing pilot overhead [6] [7]. In addition, a channel estimation method based on a block sparse compressed sensing algorithm is proposed, by taking advantage of the feature that non-zero values in the angle domain matrix of mmWave channels are distributed in blocks [8].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Liu

Sun

et al. 2023

Physical Communication

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Section: Introductionmentioning

confidence: 99%

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Liu

Sun

et al. 2023

Physical Communication

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“…Computational complexity is tremendously alleviated because the one-dimensional overcomplete dictionary in OGOMP algorithm is established only around initial DOA estimates. Traditional OMP algorithm [19] requires that all target signals must fall on a preset grid. However, in actual engineering applications, no matter how the grid is divided, it is impossible to ensure that all target signals fall exactly on the grid.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Vector Sparse Nested Array: Array Structure Design, Off-Grid Parameter Estimation Algorithm

Zhu

Chen

2021

Mathematical Problems in Engineering

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In this paper, a new array structure of sparse nested array (SNA) for electromagnetic vector sensor is designed. An electromagnetic vector sensor is composed of six spatially colocated, orthogonally oriented, diversely polarized antennas, which can measure three-dimensional electric and magnetic field components. By introducing sparse factor (SF) between every adjacent sensor, the proposed SNA has flexibility of extending the array aperture and reducing the mutual coupling effect. Meanwhile, a low-complexity multiparameter estimation algorithm is proposed for SNA. First, the vectorization operation for array manifold ensures the large degrees of freedom for multiparameter estimation, where the initial coarse estimates decrease search range. In addition, the improved off-grid orthogonal matching pursuit method obtains joint direction of arrival (DOA) and polarization estimates with a relatively small overcomplete dictionary because this off-grid method achieves high performance even if the estimates do not fall on the grid of the dictionary. Theoretical analysis and simulation results verify the superiority of the proposed array structure and the algorithm.

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Sparse channel estimation using orthogonal matching Pursuit algorithm for SCM-OFDM system

Cited by 2 publications

References 7 publications

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Electromagnetic Vector Sparse Nested Array: Array Structure Design, Off-Grid Parameter Estimation Algorithm

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