Sparse-Based Millimeter Wave Channel Estimation With Mutual Coupling Effect

Cao, Zhenxin; Geng, Haiyang; Chen, Zhimin; Chen, Peng

doi:10.3390/electronics8030358

Cited by 6 publications

(8 citation statements)

References 30 publications

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“…In the traditional methods to exploit the target sparsity, the compressed sensing-(CS-) based methods have been proposed [49], where the dictionary matrix is formulated by discretizing the spatial domain. en, the DOA information is obtained from the dictionary matrix.…”

Section: Continue Domain Super-resolution Estimation Methodsmentioning

confidence: 99%

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

Liu

Tang

Bai

et al. 2020

Mathematical Problems in Engineering

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Direction of arrival (DOA) estimation is an essential problem in the radar systems. In this paper, the problem of DOA estimation is addressed in the multiple-input and multiple-output (MIMO) radar system for the fast-moving targets. A virtual aperture is provided by orthogonal waveforms in the MIMO radar to improve the DOA estimation performance. Different from the existing methods, we consider the DOA estimation method with only one snapshot for the fast-moving targets and achieve the superresolution estimation from the snapshot. Based on a least absolute shrinkage and selection operator (LASSO), a denoise method is formulated to obtain a sparse approximation to the received signals, where the sparsity is measured by a new type of atomic norm for the MIMO radar system. However, the denoise problem cannot be solved efficiently. en, by deriving the dual norm of the new atomic norm, a semidefinite matrix is constructed from the denoise problem to formulate a semidefinite problem with the dual optimization problem. Finally, the DOA is estimated by peak-searching the spatial spectrum. Simulation results show that the proposed method achieves better performance of the DOA estimation in the MIMO radar system with only one snapshot.(DFT) [19] is used to estimate the DOA, where the received signals are sampled by the antennas in the spatial domain, and then the DOA estimation is equal to a corresponding frequency estimation in the transformed domain. erefore, the frequency (DOA) in the spatial domain is obtained by the DFT methods, but the resolution of DFT method is limited by Rayleigh criterion. e methods that can break through the Rayleigh criterion are called super-resolution methods. Multiple signal classification (MUSIC) method [20][21][22], Root-MUSIC [23], and the estimation of signal parameters via rotational invariant techniques (ESPRIT) method [24][25][26] are three most essential super-resolution methods. e noise subspace and signal subspace are obtained in the MUSIC and ESPRIT methods to estimate the DOA, respectively. A TOD-MUSIC algorithm is proposed in [27] to estimate the DOA in the scenario with low signal-to-noise ratio (SNR) with diversity bistatic MIMO radar.However, the subspaces are obtained from the estimated covariance matrix of the received signals, so the multiple measurements are needed in MUSIC and ESPRIT methods to achieve a reasonable estimation of the covariance matrix.

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Section: Continue Domain Super-resolution Estimation Methodsmentioning

confidence: 99%

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

Liu

Tang

Bai

et al. 2020

Mathematical Problems in Engineering

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“…= M and u i ∈ {0, 1} as an indicator function is specified by the array structure, which can be uniform or nonuniform antenna array. 22,23 The dominant path in mmWave communication is the LoS path. If the LoS does not exist for the reasons such as blockage by obstacles, the NLoS path with highest gain will be the dominant path.…”

Section: System Modelmentioning

confidence: 99%

Millimeter‐wave non‐orthogonal multiple access systems with sparse antenna arrays

Azhiri

Tazehkand

Abdolee

2022

Trans Emerging Tel Tech

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Millimeter wave (mmWave) non‐orthogonal multiple access (NOMA) is a multiuser transmission scheme that can increase the quality and data rate of wireless mobile networks. In this article, we introduce an mmWave‐NOMA system utilizing a sparse antenna array that not only offers small and lightweight antenna devices in the base station but also improves the outage probability performance of the system. We also introduce a low‐complex computation technique to find the optimum beamforming vector to reduce the outage probability. We provide a proof of convexity for the outage probability of the mobiles' communication links in the proposed system for which a gradient‐based algorithm can be used to find the optimum solution. The simulation results show that utilizing the proposed optimum beamforming vector reduces the outage probability of the system up to 98% in high signal‐to‐noise ratios. Our simulation and analysis results demonstrate that the system equipped with a sparse antenna array applying the optimum beamforming vector outperform conventional mmWave‐NOMA system with uniform linear array antennas in terms of outage probability and sum rates.

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“…In the first approach, the mmWave channel is estimated by exploiting the sparsity of the channel matrix in the virtual beamspace domain, whereas, in the second approach, the estimation is performed by exploiting the low rank properties of the channel matrix in the antenna domain. In [11][12][13][14], a compressive sensing (CS)-based channel estimation approach was used for a mmWave MIMO system. The basic idea behind this approach is based on the technique in which estimators have to search for a pair angle in a predefined dictionary matrix, which further depends upon the training information.…”

Section: Introductionmentioning

confidence: 99%

Precise Channel Estimation Approach for a mmWave MIMO System

2020

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Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to exploit low rank and channel sparsity individually in the antenna domain. Moreover, a relaxation factor is introduced which improves the proposed algorithm’s convergence. Simulation experiments illustrate that the proposed algorithm converges at lower Normalized Mean Squared Error (NMSE) with improved spectral efficiency. The proposed algorithm also ameliorates NMSE performance at low, mid and high Signal to Noise (SNR) ranges.

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Sparse-Based Millimeter Wave Channel Estimation With Mutual Coupling Effect

Cited by 6 publications

References 30 publications

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

Millimeter‐wave non‐orthogonal multiple access systems with sparse antenna arrays

Precise Channel Estimation Approach for a mmWave MIMO System

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