Adaptive DOA estimation with low complexity for wideband signals of massive MIMO systems

Qiang, Xiaowei; Liu, Yang; Feng, Qingxia; Zhang, Yinghui; Qiu, Tianshuang; Jin, Minglu

doi:10.1016/j.sigpro.2020.107702

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…To deal with the multi-DOA estimation problem, we also use the PLS to decrease the scale of dataset space and obtain multiclassification results with high performance. We denote the training data as D = R (1) , z z z (1) , R (2) , z z z (2)…”

Section: Curriculum Learning and Partial Labelmentioning

confidence: 99%

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Multi-DOA Estimation Based on the KR Image Tensor and Improved CNN

Yuan

Yang

et al. 2021

Preprint

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In this paper, we propose an improved convolutional neural network (CNN) to solve the multi-DOA estimation problem. We use Khatri-Rao (KR) product to obtain the KR image tensor of covariance matrix and use the proposed estimation CNN to process the tensor. In order to increase the generalization of the proposed CNN and adapt the multi-label classification problem, we use the curriculum learning scheme (CLS) and partial label strategy (PLS) to develop an efficient training procedure. We implement several experiments to demonstrate the satisfying performance of the proposed estimation method. The simulation results show that our proposed method can finish the high resolution multi-DOA estimation use only a few sensors. Furthermore, the proposed method can obtain high estimation accuracy under low SNR situations and use fewer snapshots.

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Section: Curriculum Learning and Partial Labelmentioning

confidence: 99%

“…Direction-of-Arrival (DOA) estimation technologies are widely used in the modern radar and communication systems [1][2][3][4][5] . In the past decades, researchers have presented many super-resolution estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-DOA Estimation Based on the KR Image Tensor and Improved CNN

Yuan

Yang

et al. 2021

Preprint

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“…It is generally known that the performance of parameter detection highly depends on the accurate determination of the number of target signals [4]. Through an extensive literature study and survey, we find that the commonly used methods for detection of the number of target signals are the Minimum Description Length (MDL) [5], Akaike Information Criterion (AIC) [6], and Gerschgorin Disk Estimator (GDE) [7]. However, these methods perform poorly under some non-ideal conditions (say, low Signal-to-Noise Ratio (SNR) and small snapshots).…”

Section: Introductionmentioning

confidence: 99%

Number of targets detection method with FCM‐based granulation–degranulation mechanism

Sha

Mao

et al. 2022

Electronics Letters

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In this letter, the authors elaborate on a novel method to detect the number of targets in low SNR and small snapshots through using the Fuzzy C‐Means clustering based granulation‐degranulation mechanism. In the developed scheme, the eigenvalues of the array output correlation matrix are regarded as a time series, and granulated into a pair of ordered information granules. The information about the number of targets is included in the information granules. Subsequently, the degranulation mechanism is used to modify the cost function of the granulation mechanism to improve the quality of the information granules, which can increase the detection accuracy of the number of targets. Finally, the number of targets is determined by the prototype matrix and partition matrix generated by the enhanced version of the granulation. Explicit analysis and derivation of the proposed scheme are presented.

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“…Base stations rely on massive MIMO systems to uplink sounding signals, and compute channel knowledge to enable MIMO beam-forming [ 14 , 15 , 16 ]. Therefore, direction-of-arrival (DOA) estimation is important for massive MIMO], as it is needed to complete the predicted capacity gains [ 17 , 18 ]. Various DOA estimation algorithms have been proposed in recent years, such as improved multiple signal classification (MUSIC) [ 19 ], estimation of signal parameters via rotational invariance techniques (ESPRIT) [ 20 ], and the propagator method (PM) [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array

Gong

Chen

2021

Sensors

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In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer–Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms.

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Adaptive DOA estimation with low complexity for wideband signals of massive MIMO systems

Cited by 15 publications

References 37 publications

Multi-DOA Estimation Based on the KR Image Tensor and Improved CNN

Multi-DOA Estimation Based on the KR Image Tensor and Improved CNN

Number of targets detection method with FCM‐based granulation–degranulation mechanism

Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array

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