Low-Complexity Deep-Learning-Based DOA Estimation for Hybrid Massive MIMO Systems With Uniform Circular Arrays

Hu, Die; Zhang, Yonghao; He, Lianghua; Wu, Jun

doi:10.1109/lwc.2019.2942595

Cited by 80 publications

(45 citation statements)

References 10 publications

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“…Also note that the coefficients of the form are real, while those of the later are complex. Similar to NC-root-MUSIC, we can find signal DOAs by inserting the roots of f RV−NC−root−MUSIC that lies cloest to and inside the unit cycle into (15). The real coefficients of z k in f RV−NC−root−MUSIC and its roots have an important property that is different from those of f NC−root−MUSIC , which is given by the following theorem.…”

Section: A Proposed Doa Estimation With Real-valued Computationsmentioning

confidence: 99%

Real-Valued DOA Estimation for Non-Circular Sources via Reduced-Order Polynomial Rooting

et al. 2019

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A novel real-valued algorithm of the popular root multiple signal classification of noncircular sources (NC-root-MUSIC) is developed for low-complexity direction of arrival (DOA) estimation. The proposed real-valued NC-root-MUSIC (RV-NC-root-MUSIC) reduces the computational burden mainly in following three aspects. First, unlike the classical real-valued algorithms, such as unitary transform, the complex-valued operation is still implemented when acquiring DOAs. However, the proposed RV-NC-root-MUSIC implements real-valued received data. Therefore, both tasks of eigenvalue decomposition (EVD) (or singular value decomposition (SVD)) and polynomial rooting can be implemented completely by real-valued computations in the proposed RV-NC-root-MUSIC. Next, due to the conjugate expansion of the non-circular sources, the steering vector dimension is extended by a factor of two, and the real-valued signal-and noise-subspace can be calculated by two reduced-dimension sub-matrices. As such, large-scale EVD/SVD computation is avoided. Finally, a real coefficient rooting polynomial with a similar order as NC-root-MUSIC is constructed. A variable substitution technique is further proposed to reduce the degree of this polynomial by half without sacrificing accuracy, which allows fast coefficient computation and realvalued rooting. The real-valued computation and reduced-order polynomial rooting in the proposed method result in a significant reduction in computational cost as compared to state-of-the-art methods. Numerical computer simulations illustrate that with significantly reduced complexity, the fast RV-NC-root-MUSIC algorithm can provide similar root mean square errors (RMSE) close to the conventional methods. INDEX TERMS Direction of arrival (DOA), non-circular sources, real-valued computations, reduced-order, fast rooting.

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Section: A Proposed Doa Estimation With Real-valued Computationsmentioning

confidence: 99%

Real-Valued DOA Estimation for Non-Circular Sources via Reduced-Order Polynomial Rooting

et al. 2019

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“…With the rapid development of artificial intelligent (AI) technologies 32 , some AI based estimation methods have been proposed in recent years. Researchers introduce the deep neural network (DNN) into the DOA estimation field and develop a DNN group to do the uniform linear array (ULA) estimation task 33 .…”

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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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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“…A cognitive radar scenario is considered in [8] where DL is applied for sparse array selection and DOA estimation for a single target and this work is extended for two targets in [12]. Apart from these, DOA estimation is also considered in [7] for massive MIMO scenario where a simple MLP structure is proposed for the estimation of a line-of-sight path angle of a single user. In addition to these studies, a short review of DL for the applications in electromagnetics is provided in [13].…”

Section: Introductionmentioning

confidence: 99%

“…We design multiple CNNs, each of which is dedicated for a subregion of the angular spectrum. Hence, we partition the angular spectrum into non-overlapping subregions [5], [7], and assume that there is a single target in each subregion. Then each deep network is fed with the array covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

DeepMUSIC: Multiple Signal Classification via Deep Learning

Elbir

2020

IEEE Sens. Lett.

104

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This letter introduces a deep learning (DL) framework for direction-of-arrival (DOA) estimation. Previous works in DL context mostly consider a single or two target scenario which is a strong limitation in practice. Hence, in this work, we propose a DL framework for multiple signal classification (DeepMUSIC). We design multiple deep convolutional neural networks (CNNs), each of which is dedicated to a subregion of the angular spectrum. In particular, each CNN is fed with the array covariance matrix and it learns the MUSIC spectra of the corresponding angular subregion. We have shown, through simulations, that the proposed DeepMUSIC framework has superior estimation accuracy and exhibits less computational complexity in comparison with both DL and non-DL based techniques.

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Low-Complexity Deep-Learning-Based DOA Estimation for Hybrid Massive MIMO Systems With Uniform Circular Arrays

Cited by 80 publications

References 10 publications

Real-Valued DOA Estimation for Non-Circular Sources via Reduced-Order Polynomial Rooting

Real-Valued DOA Estimation for Non-Circular Sources via Reduced-Order Polynomial Rooting

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

DeepMUSIC: Multiple Signal Classification via Deep Learning

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