High-performance Passive Eigen-model-based Detectors of Single Emitter Using Massive MIMO Receivers

Jie, Qijuan; Zhan, Xichao; Ding, Yaohui; Shi, Baihua; Li, Yifan; Wang, Jiangzhou

doi:10.48550/arxiv.2108.02011

Cited by 4 publications

(8 citation statements)

References 13 publications

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“…Their closed-form expressions were presented and the corresponding detection performance was given. As shown in [15], the proposed R-MaxEV-NV method performs much better than the traditional generalized likelihood ratio test (GLRT) method with a fixed false alarm probability in terms of receiver operating characteristic curve (ROC).…”

Section: Proposed Multi-layer Neural Network Detector For Multi-emittermentioning

confidence: 96%

“…To achieve a high detection performance of emitter and trigger the next step: DOA measurements, a high-performance detector was proposed to infer the existence of multi-emitter from the eigen-space of sample covariance matrix of receive signal vector [15]. Here, the sampling covariance of receive signal vector was first computed, and its EVD was performed to extract all its eigenvalues.…”

Section: Proposed Multi-layer Neural Network Detector For Multi-emittermentioning

confidence: 99%

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Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

Feng¹,

Zhan²,

Cai³

et al. 2022

Preprint

Self Cite

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Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a key technology for future wireless networks. Combining a massive HAD-MIMO with direction of arrinal (DOA) will provide a high-precision even ultra-high-precision DOA measurement performance approaching the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we review three aspects: detection, estimation, and Cramer-Rao lower bound (CRLB) with low-resolution ADCs at receiver. First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of passive emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to eliminate phase ambiguity using only one-snapshot. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB using single snapshot.

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Section: Proposed Multi-layer Neural Network Detector For Multi-emittermentioning

confidence: 96%

Section: Proposed Multi-layer Neural Network Detector For Multi-emittermentioning

confidence: 99%

Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

Feng¹,

Zhan²,

Cai³

et al. 2022

Preprint

Self Cite

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show abstract

“…The simulation results show that, SR-MME and GM have significant improvement in detection performance compared with MME detector proposed in [16] and M-MME detector proposed in [23], even SNR is very low and number of samples is small. The simulation results also show that SR-MME and GM can maintain a low false alarm probability while achieving a high detection probability.…”

Section: Introductionmentioning

confidence: 92%

“…As is shown in Fig. 2, the input of this neural network is feature vector defined in (23), the input layer is constructed of 5 neurons. Since there are most K emitters in the coverage area of base station, the number of neurons in output layer is also K and the outputs of these neurons are denoted by {ĝ 1 , ĝ2 , • • • , ĝK }.…”

Section: B Proposed Multi-layer Neural Network Classifiermentioning

confidence: 99%

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Machine Learning Methods for Inferring the Number of Passive Emitters via Massive MIMO Receive Array

Li¹,

Jie²,

Song³

et al. 2022

Preprint

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To improve the efficiency and accuracy of direction finding with massive MIMO receive array, it is necessary to determine the specific number of signal emitters in advance. In this paper, we present a complete DOA preprocessing system for inferring the number of passive emitters. Firstly, in order to improve the accuracy of detecting the number of signals, two high-precision signal detectors, square root of maximum eigenvalue times minimum eigenvalue (SR-MME) and geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number, that is a problem of pattern classification. Therefore, we perform feature extraction on the the eigenvalue sequence of sample covariance matrix to construct feature vector and innovatively propose a multilayer neural network (ML-NN). Additionally, the support vector machine (SVM), and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learningbased methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and Minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive receive arrays, and they also have much worse performance than machine learning-based classifiers.

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Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array

Yan

et al. 2023

Drones

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To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.

show abstract

High-performance Passive Eigen-model-based Detectors of Single Emitter Using Massive MIMO Receivers

Cited by 4 publications

References 13 publications

Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

Machine Learning Methods for Inferring the Number of Passive Emitters via Massive MIMO Receive Array

Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array

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