Performance Analysis of OMP-Based Channel Estimations in Mobile OFDM Systems

Tan, Guoping; Wu, Bingyang; Herfet, Thorsten

doi:10.1109/twc.2018.2813380

Cited by 18 publications

(17 citation statements)

References 18 publications

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“…An interesting property is that the DFT of a ZC sequence has a constant magnitude, i.e., |U M ξ| = 1 [27]. When g is set to ξ, we observe that the unimodular DFT condition in (29) holds when q = 1. Now, we notice from (30) that g q = ξ q has the same structure as ξ, but with root qu instead of u.…”

Section: How To Design a Good Subsampling Trajectory?mentioning

confidence: 87%

“…From (3) and (14), we see that the amplitude of all the diagonal elements in Λ(δ) is √ N δ r except the first one. Therefore, denoting A S as the matrix formed by the support columns (defined as the final set of dominant columns chosen from the dictionary), the pseudoinverse of matrix A S as A + S = (A * S A S ) −1 A * S , the net radar SNR as ζ net (ρ, δ) = N δ r ζ p [ρ], the NMSE for estimating the Dopplerangle channel vectorh corresponding to the MRR/SRR targets can be approximated similar to [29] as…”

Section: A Radar Performance Metricmentioning

confidence: 99%

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A Combined Waveform-Beamforming Design for Millimeter-Wave Joint Communication-Radar

Kumari

Myers

Vorobyov

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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Millimeter-wave (mmWave) joint communicationradar (JCR) will enable high data rate communication and highresolution radar sensing for applications such as autonomous driving. Prior JCR systems that are based on the mmWave communications hardware, however, suffer from a limited angular field-of-view and low estimation accuracy for radars due to the employed directional communication beam. In this paper, we propose an adaptive and fast combined waveformbeamforming design for the mmWave automotive JCR with a phased-array architecture that permits a trade-off between communication and radar performances. To rapidly estimate the mmWave automotive radar channel in the Doppler-angle domain with a wide field-of-view, our JCR design employs a few circulant shifts of the transmit beamformer and apply twodimensional partial Fourier compressed sensing technique. We optimize these circulant shifts to achieve minimum coherence in compressed sensing. We evaluate the JCR performance trade-offs using a normalized mean square error (MSE) metric for radar estimation and a distortion MSE metric for data communication, which is analogous to the distortion metric in the rate-distortion theory. Additionally, we develop a MSE-based weighted average optimization problem for the adaptive JCR combined waveformbeamforming design. Numerical results demonstrate that our proposed JCR design enables the estimation of short-and medium-range radar channels in the Doppler-angle domain with a low normalized MSE, at the expense of a small degradation in the communication distortion MSE.

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Section: How To Design a Good Subsampling Trajectory?mentioning

confidence: 87%

Section: A Radar Performance Metricmentioning

confidence: 99%

A Combined Waveform-Beamforming Design for Millimeter-Wave Joint Communication-Radar

Kumari

Myers

Vorobyov

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

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“…The NMSE for estimating the masked Doppler-angle channel vectorz(δ) in (20) corresponding to the dominant channel taps using the optimized sampling trajectory in (34) and the OMP estimation can be approximated similar to the NMSE derivation in [43]. From (3) and ( 14), we see that the amplitude of all the diagonal elements in Λ(δ) is √ N δ r except the first one.…”

Section: A Radar Performance Metricmentioning

confidence: 99%

Adaptive and Fast Combined Waveform-Beamforming Design for MMWave Automotive Joint Communication-Radar

Kumari

Myers

Heath

2021

IEEE J. Sel. Top. Signal Process.

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Millimeter-wave (mmWave) joint communicationradar (JCR) will enable high data rate communication and highresolution radar sensing for applications such as autonomous driving. Prior JCR systems that are based on the mmWave communications hardware, however, suffer from a limited angular field-of-view and low estimation accuracy for radars due to the employed directional communication beam. In this paper, we propose an adaptive and fast combined waveform-beamforming design for the mmWave automotive JCR with a phased-array architecture that permits a trade-off between communication and radar performances. To rapidly estimate the mmWave automotive radar channel in the Doppler-angle domain with a wide field-of-view, our JCR design employs circulant shifts of the transmit beamformer to acquire radar channel measurements and uses two-dimensional compressed sensing (CS) in the spacetime dimension. We optimize these circulant shifts to minimize the coherence of the CS matrix, under the space-time sampling constraints in our problem. We evaluate the JCR performance trade-offs using a normalized mean square error (MSE) metric for radar estimation and a distortion MSE metric for data communication, which is analogous to the distortion metric in the rate-distortion theory. Additionally, we develop a MSEbased weighted average optimization problem for the adaptive JCR combined waveform-beamforming design. Numerical results demonstrate that our proposed JCR design enables the estimation of short-and medium-range radar channels in the Doppler-angle domain with a low normalized MSE, at the expense of a small degradation in the communication distortion MSE.

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“…Usually, the CS-based methods can be classified into the greedy methods and the norm-based methods: (1) in the greedy methods, such as orthogonal matching pursuits (OMP) [41], stagewise OMP (StOMP), and CoSaMP [42], iterations are used to reconstruct the sparse signals; (2) in the norm-based method, the ℓ 0 norm minimization problem is transformed into a ℓ 1 norm minimization problem, which can be solved efficiently with the convex optimization tools. Additionally, sparse Bayesian learning-(SBL-) based methods with the prior assumption of sparse signals are also propped [43], such as SBL method and OGSBI method [44], which can achieve the excellent performance with relatively high computational complexity.…”

Section: Introductionmentioning

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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Performance Analysis of OMP-Based Channel Estimations in Mobile OFDM Systems

Cited by 18 publications

References 18 publications

A Combined Waveform-Beamforming Design for Millimeter-Wave Joint Communication-Radar

A Combined Waveform-Beamforming Design for Millimeter-Wave Joint Communication-Radar

Adaptive and Fast Combined Waveform-Beamforming Design for MMWave Automotive Joint Communication-Radar

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

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