DeepMUSIC: Multiple Signal Classification via Deep Learning

Elbir, Ahmet M.

doi:10.1109/lsens.2020.2980384

Cited by 136 publications

(48 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this model does not perform well in an environment with a low signal-to-noise ratio and color noise. Elbir [44] designed multiple CNNs, and such that each CNN is dedicated to an angular spectrum to learn the multiple signal classification (MUSIC) spectra of the corresponding angle subregion. is method reduces the amount of calculation and improves the accuracy of DOA estimation.…”

Section: Doa Estimation In Signalmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

View full text Add to dashboard Cite

In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Google Scholar) for exploration. Six questions describing the overall trend of DOA estimation using deep learning are put forward. Then, we answered these questions by reviewing the literature. This review is helpful for researchers in this field because it provides more specific and comprehensive information needed for future research. Specifically, we first analyzed the background of the selected papers, including the type of publication, the number of citations, and the country of origin. Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and combination network), and various DOA estimation schemes. Finally, various evaluation criteria (root-mean-squared error, accuracy, and mean absolute error) are used to evaluate the DL technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas, and number of signal sources) affecting DOA estimation are analyzed. Based on our findings, we believe that deep learning can perform DOA estimation well, and there is still room for improvement in deep learning technology. In this study, the factors affecting DOA estimation can be used as the direction for researchers to conduct in-depth research.

show abstract

Section: Doa Estimation In Signalmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…f (2,3) f (2,4) f (2,5) f (2,6) f (2,7) f (2,8) f (2,9) f (2,10) f (2,11) f (2,12) f (1,2) f (1,3) f (1,4) f (1,5) f (1,6) f ( cessed with an N r ×N s fully digital combiner W B . The combined signal can be represented as y = W H B Hx + W H B n, where n denotes the additive noise vector satisfying the complex circularly symmetric Gaussian distribution with zero mean and covariance matrix σ 2 I Nr , i.e., n ∼ CN 0, σ 2 I Nr .…”

Section: System Modelmentioning

confidence: 99%

Channel Estimation and Hybrid Precoding for Millimeter Wave Communications: A Deep Learning-Based Approach

Qiujin

Lin

Zhu³

2021

IEEE Access

View full text Add to dashboard Cite

Hybrid analog and digital beamforming (HBF) has been regarded as a key technology for future millimeter wave (mmWave) communication systems due to its ability to obtain a good tradeoff between achievable beamforming gain and hardware cost. In this paper, we investigate the channel estimation and hybrid precoding for mmWave MIMO systems with deep learning. We adopt the hierarchical codebook based algorithm for channel estimation as it requires limited number of pilot transmissions, and enhance its performance by proposing a new codebook design algorithm based on manifold optimization (MO). With the estimated channel state information (CSI) as the input, we develop a robust HBF network (HBF-Net) by applying convolutional layers and attention mechanism, which can be trained to generate a robust HBF matrix targeting at spectral efficiency maximization with imperfect CSI. To further improve the performance, we propose a joint channel estimation and HBF optimization network (CE-HBF-Net). Considering that the adaptively selected HBF vectors in the hierarchical codebook based channel estimation are different for different channel realizations, we skillfully propose an index assign-and-input method to efficiently feed such information to the CE-HBF-Net to reduce the network input dimensions and make the network trainable. Furthermore, we propose a signal self-attention mechanism to enable the CE-HBF-Net to intelligently assign larger weight coefficients to those signals that contribute more to channel estimation. Simulation results show that the well-designed HBF-Net and CE-HBF-Net outperform the conventional HBF algorithms with imperfect channel and exhibit robustness to mismatches between offline training and online deployment stages. INDEX TERMSMillimeter wave (mmWave), channel estimation, hierarchical codebook, manifold optimization (MO), hybrid beamforming (HBF), deep learning (DL), attention mechanism

show abstract

“…With the rapid development of artificial intelligence (AI), researchers have designed many deep-learning-based (DLbased) networks to achieve data-driven estimation approaches [16]- [18]. There are generally two kinds of models to realize DL-based estimation: the classification model and the regression model [19], [20]. The classification models (such as deep neural network (DNN) group [14] used to against array imperfections and convolutional neural network (CNN) designed for multi-speaker DOA estimation [21]) divides the angle space into many discrete subregions, then use the classifier networks to estimate DOAs in each region.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Yuan¹,

Wu²,

Wu³

et al. 2021

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

In this letter, we proposed a novel unsupervised learning strategy for direction-of-arrival (DOA) estimation network. Inspired by the sparse power spectrum and 1-norm optimization, we develop a novel loss function to cooperate with the estimation network. Unlike the prior DL-based methods, the proposed method does not need any manual annotations for training and validation datasets. Compared with state-of-art methods, the proposed method can automatically increase the degree of freedom of the array without further pre-processing on the covariance matrix of array observation data. Moreover, the proposed method can obtain clear spectrum and precise DOAs under harsh estimation environments.

show abstract

DeepMUSIC: Multiple Signal Classification via Deep Learning

Cited by 136 publications

References 17 publications

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Channel Estimation and Hybrid Precoding for Millimeter Wave Communications: A Deep Learning-Based Approach

Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Contact Info

Product

Resources

About