Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading

Chen, Teng; Liao, Ching-Chun; Chen, Chun-Hsiang; Wu, An-Yeu

doi:10.1109/globalsip.2018.8646375

Cited by 20 publications

(19 citation statements)

References 13 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Teng et al [5] extend their previous work in [91] and proposed an accumulated polar feature-based DL with a channel compensation mechanism. In [91], they have shown that learning features from polar coordinates, which can be obtained from Cartesian coordinates, can achieve higher recognition accuracy. Then in [5], they add a new temporal axis to accumulate historical information of symbols in such dimension.…”

Section: D: Classification Using Other Inputsmentioning

confidence: 83%

“…Two mechanisms for online retraining were introduced to deal with the timevarying fading channel while having lower transmission and retraining overhead. The model reported in [91] was used as a benchmark. Results revealed that the proposed method can reduce the offline training overhead by about 190 times compared to [91] and, therefore, provide better efficiency and accuracy.…”

Section: D: Classification Using Other Inputsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

View full text Add to dashboard Cite

The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

show abstract

Section: D: Classification Using Other Inputsmentioning

confidence: 83%

Section: D: Classification Using Other Inputsmentioning

confidence: 99%

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It is measured by computing the ratio of the correctly estimated modulation scheme to the original modulation scheme of the received signal. It can be approximated as follows: (32) where 1 represents the true positive, 2 represents the true negative, ℱ 1 represents the false positive, and ℱ 2 represents the false negative. As the work is about multi-class classification, we construct confusion matrix by considering actual class and predicted class for all classes.…”

Section: Methodsmentioning

confidence: 99%

“…Nonetheless, the performance of the AEs decreased for a large-scale dataset due to high computation processes. Chieh-Fang et al [32] exploited a channel compensation network (CCN) to detect the modulation type of the signal. Polar features were learnt through polar transform.…”

Section: Amc With MLmentioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

View full text Add to dashboard Cite

The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

show abstract

“…With more and more revolutionary breakthroughs in the fields of computer vision and natural language processing, machine learning-assisted communication systems have gradually attracted attention in recent years [1]- [2]. For example, a powerful modulation classifier is proposed in [3]- [5] by taking advantages of convolutional neural networks. For channel decoding, the authors in [6]- [8] propose neural network-based decoders for BCH code and polar code, which have better performance and faster convergence.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-based Equalizer by Utilizing Coding Gain in Advance

Chen

2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Self Cite

View full text Add to dashboard Cite

Recently, deep learning has been exploited in many fields with revolutionary breakthroughs. In the light of this, deep learning-assisted communication systems have also attracted much attention in recent years and have potential to break down the conventional design rule for communication systems. In this work, we propose two kinds of neural network-based equalizers to exploit different characteristics between convolutional neural networks and recurrent neural networks. The equalizer in conventional block-based design may destroy the code structure and degrade the capacity of coding gain for decoder. On the contrary, our proposed approach not only eliminates channel fading, but also exploits the code structure with utilization of coding gain in advance, which can effectively increase the overall utilization of coding gain with more than 1.5 dB gain.

show abstract

Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading

Cited by 20 publications

References 13 publications

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

Neural Network-based Equalizer by Utilizing Coding Gain in Advance

Contact Info

Product

Resources

About