Bilinear Matrix Factorization Methods for Time-Varying Narrowband Channel Estimation: Exploiting Sparsity and Rank

Beygi, Sajjad; Elnakeeb, Amr; Choudhary, Sunav; Mitra, Urbashi

doi:10.1109/tsp.2018.2872886

Cited by 16 publications

(23 citation statements)

References 32 publications

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“…To show the low-rank structure of the UWA channel, we first consider the case that the delay and Doppler are separable in the spreading function, then Sh(τ,v) can be expressed as Sh(τ,v)=∑p=1Pfp(τ)gp(v) where fp(τ) and gp(v) describe the delay and Doppler profile of the p th dominant path, respectively. For sparse channel models considered in the literature, fp(τ) and gp(v) are usually assumed to be of a ’sharp’ shape function with a small spread, such as the Dirac delta function or sinc function [10,11,16]. In this paper, we did not have such restriction, and the model in (9) generally takes the case of large dispersion spreads into account.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…One well-known scheme is the least-squares (LS) estimator. However, due to lack of exploiting any prior information of the channel, LS estimators usually require many training signal measurements to achieve good estimation, especially when the number of unknown parameters is large [10]. Therefore, various channel estimation schemes have been proposed by considering the sparsity of channels, in which the technique of compressed sensing (CS) plays an important role [11].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery

Chen

Liu

et al. 2019

Sensors

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In this paper, the estimation of overspread, i.e., doubly spread underwater acoustic (UWA) channels of strong dispersion is considered. We show that although the UWA channel dispersion causes the degeneration of channel sparsity, it leads to a low-rank structure especially when the channel delay-Doppler-spread function is separable in delay and Doppler domain. Therefore, we introduce the low-rank criterion to estimate the UWA channels, which can help to improve the estimation performance in the case of strong dispersion. The estimator is based on the discrete delay-Doppler-spread function representation of channel, and is formulated as a low-rank matrix recovery problem which can be solved by the singular value projection technique. Simulation examples are carried out to demonstrate the effectiveness of the proposed low-rank-based channel estimator.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery

Chen

Liu

et al. 2019

Sensors

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“…Channel leakage has been addressed by enhancing classical sparse approximation [3,10,11]. Distinct from these approaches, [12] exploits the atomic norm heuristic [13,14] to promote structure while explicitly considering the leakage leading to strong performance improvements over [10]. The extensions of [12] to the orthogonal frequency-division multiplexing leaked channel are presented in [15] - [17].…”

Section: Introductionmentioning

confidence: 99%

Improved Atomic Norm Based Channel Estimation for Time-Varying Narrowband Leaked Channels

Mitra

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, improved channel gain delay estimation strategies are investigated when practical pulse shapes with finite block length and transmission bandwidth are employed. Pilot-aided channel estimation with an improved atomic norm based approach is proposed to promote the low rank structure of the channel. All the channel parameters, i.e., delays, Doppler shifts and channel gains are recovered. Design choices which ensure unique estimates of channel parameters for root-raised-cosine pulse shapes are examined. Furthermore, a perturbation analysis is conducted. Finally, numerical results verify the theoretical analysis and show performance improvements over the previously proposed method.

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“…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. Moreover, different from the traditional methods of the sparse reconstruction using the discretized dictionary matrix, the atomic norm-based method as a new type of the norm-based methods realizes the sparse reconstruction in the continuous domain [45][46][47]. In [48], a semidefinite programming-based method is proposed for the ℓ 1 norm optimization over infinite dictionaries.…”

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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Bilinear Matrix Factorization Methods for Time-Varying Narrowband Channel Estimation: Exploiting Sparsity and Rank

Cited by 16 publications

References 32 publications

Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery

Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery

Improved Atomic Norm Based Channel Estimation for Time-Varying Narrowband Leaked Channels

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

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