Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation

Lin, Meiyan; Zhang, Xiaoxu; Tian, Ye; Huang, Yonghui

doi:10.3390/s22103909

Cited by 11 publications

(6 citation statements)

References 50 publications

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“…The more general task of wideband signal detection has been the subject of fewer research works. Among them, some of the proposed methods focus solely on detecting the presence of narrowband signals in wide frequency bands [20], [21], others enable both detection and localization in time and frequency domains [22], [23], [24], while other works offer classification capabilities on top of detection and localization. In the latter category, research efforts have mainly focused on attributing signals to specific wireless technologies.…”

Section: B Related Workmentioning

confidence: 99%

An End-to-End Deep Learning Framework for Wideband Signal Recognition

Vagollari¹,

Hirschbeck²,

Gerstacker³

2023

IEEE Access

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Successful management of the radio spectrum requires, as a first step, detailed information about spectrum occupancy. In this work, we present an end-to-end deep learning (DL) based framework to obtain information from wide spectrum bands through signal detection, localization, and modulation classification. By visually representing the radio signals in spectrograms, we formulate the wideband detection problem as an object detection task from the computer vision field. To this end, the proposed framework consists of two cascaded modules: an object detection network repurposed to detect and classify distinctive signals in wideband spectrograms, and a convolutional neural network (CNN) designed to extend the classification capabilities to support a wide range of analog and digital modulation schemes. To evaluate our framework, we use a public wideband recognition dataset, which we carefully analyze and curate through a series of preprocessing techniques. To tackle the challenges of insufficient training data and class imbalance observed in the dataset, we suggest a training strategy that includes data mixing and transfer learning. Our experimental results on a general test set demonstrate that the proposed approach can detect and classify a variety of narrowband signals with simultaneously high precision (77.1%), recall (81.8%), and localization accuracy, as indicated by an average Intersection over Union (IoU) of 86%.

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Section: B Related Workmentioning

confidence: 99%

An End-to-End Deep Learning Framework for Wideband Signal Recognition

Vagollari¹,

Hirschbeck²,

Gerstacker³

2023

IEEE Access

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“…The work in [10] used the correlation between physical circuit parameters and various faults and quantified the impact of each fault with respect to the healthy signatures at different frequency regions. The work in [11] proposed a deep-learning-based framework named SigdetNet, which takes the power spectrum as the network's input to localize the spectral locations of the signals. The research in [12] proposed an intrinsic time-scale decomposition (ITD)-based method for power transformer fault diagnosis based on dissolved gas analysis (DGA) parameters and used an XGBoost classifier to classify the optimal PRC feature set.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

Mao

Wei

et al. 2022

Sensors

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The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers’ three-phase fault grounding currents of the iron cores from various devices with four types of faults: Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over 95% accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices’ fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy.

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“…Inspired by fully connected networks (FCNs) [23,24] applied in two-dimensional (2D) object semantic segmentation, References [12,13] used an FCN-based model consisting of an encoder and a decoder for carrier signal detection in the broadband power spectrum. The FCN-based methods cannot correctly distinguish between the demarcation points when two or more neighboring subcarriers are very close.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, some studies [12][13][14] found that using deep learning for carrier signal detection achieves more robust and higher performance than threshold-based methods [9][10][11]. These deep-learning-based methods apply a broadband power spectrum as the input.…”

Section: Introductionmentioning

confidence: 99%

FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection

Huang

Wang

2022

Electronics

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Carrier signal detection is a complicated and essential task in many domains because it demands a quick response to the existence of several carriers in the wideband, while also precisely predicting each carrier signal’s frequency centers and bandwidths, including single-carrier and multi-carrier modulation signals. Multi-carrier modulation signals, such as FSK and OFDM, could be incorrectly recognized as several single-carrier signals by using the spectrum center net (SCN) or FCN-based method. This paper designed a deep convolutional neural network (CNN) framework for multi-carrier signal detection by fusing the features of multiple consecutive frames of the broadband power spectra and estimating the information of each single-carrier or multi-carrier modulation signal in the broadband, called frame fusion spectrum center net (FFSCN), including FFSCN-R, FFSCN-MN, and FFSCN-FMN. FFSCN includes three base parts, the deep CNN-based backbone, the feature pyramid network (FPN) neck, and the regression network (RegNet) head. FFSCN-R and FFSCN-MN fusing the FPN out features, which use the Residual and MobileNetV3 backbone, respectively, and FFSCN-MN cost less inference time. To further reduce the complexity of FFSCN-MN, the designed FFSCN-FMN modifies the MobileNet blocks and fuses the features at each block of the backbone. The multiple consecutive frames of broadband power spectra not only preserve the high-resolution ratio of the broadband frequency, but also add the features of the signal changes in the time dimension. Extensive experimental results demonstrate that the proposed FFSCN can effectively detect multi-carrier and single-carrier modulation signals in the broadband power spectrum and outperform SCN in accuracy and efficiency.

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Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation

Cited by 11 publications

References 50 publications

An End-to-End Deep Learning Framework for Wideband Signal Recognition

An End-to-End Deep Learning Framework for Wideband Signal Recognition

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection

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