Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network

Shah, Maqsood Hussain; Dang, Xiaoyu

doi:10.1049/iet-com.2018.5688

Cited by 14 publications

(12 citation statements)

References 33 publications

(38 reference statements)

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“…Compared with the model exhibiting a classification accuracy of 95% for a sample length of 2048 with four modulation types including BPSK, QPSK, 8PSK, and 16QAM when SNR is 10 dB [6], the proposed SAE model shows decent precision and generalisation capability. Moreover, the proposed model performs better when compared with a recognition accuracy of 99.6% observed in the range of 5–15 dB for seven modulation types including ASK, PSK, QAM, FSK, MSK, LFM, and OFDM in a previous study [25].…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…This proposed SAE model is called SAEs with various optimisation methods (SAE‐VOM). The model in [6] is a stacked sparse auto‐encoder based on a deep neural network (SAE‐DNN), whereas that in [25] is a stacked sparse auto‐encoder based on an ambiguity function (SAE‐AF).…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…Many scholars have proposed a variety of excellent MR methods, which can be broadly divided into two categories: likelihood‐based [4, 5] and feature‐based methods [6]. Likelihood‐based methods are based on hypothesis test theory [7], and the performance based on decision theory and Bayesian theory is optimal but exhibits high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Practical AMC model based on SAE with various optimisation methods under different noise environments

Wang

Liu

et al. 2020

IET Communications

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Automatic modulation classification (AMC) has recently attracted widespread attention nowadays due to its desirable features of generalisability and requirement of little prior knowledge through artificial intelligence (AI) technology. The authors propose a stacked auto-encoder (SAE) based on various optimisation methods structure to intelligently process a feature space that includes spectral-based features and high-order cumulants. To unify the dimensionality of the features, they apply different normalisation methods to the feature space before training the SAE model to decide corresponding normalisations under different noise environments. Linear normalisation is superior when signal-to-noise ratio (SNR) is low, and standardisation is superior when SNR is between-1 and 4 dB. Regularisation works best when SNR is greater than 5 dB. To increase the recognition accuracy of the proposed model, they introduce the unconstrained optimisation theory to adjust the proposed SAE model, including Nelder-Mead method, Newton optimisation method, conjugate gradient method and quasi-Newton method. They observe that the quasi-Newton method offers desirable performance when optimising SAE model. It is the first time to compare these data normalisation methods and discuss unconstrained optimisation theory together to recognise modulation types. The recognition accuracy of this model for eight modulation types can reach 99.8% when SNR ranges from −5 to 10 dB.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: Simulations and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Practical AMC model based on SAE with various optimisation methods under different noise environments

Wang

Liu

et al. 2020

IET Communications

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“…They employed a cyclic spectrum in order to pre-process the received signals. Also, an unsupervised SSAE-DNN-based AMR method was proposed in [120]. Its main aim was to cope with much-neglected frequency selective fading scenarios with Doppler shift.…”

Section: ) Ae-based Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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“…The received signals are preprocessed and sent to the neural network, and the network automatically extracts features and classifies. The proposed algorithm avoids the redundancy of SNR estimation and only needs to train a model, which greatly reduces the complexity of the algorithm[21]-[23].…”

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confidence: 99%

The Performance Evaluation of Big Data-Driven Modulation Classification in Complex Environment

Cai

Wang

2021

IEEE Access

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With the proliferation of frequency-using devices and the advent of the era of big data, spectrum management and control are faced with challenges of effectiveness and accuracy. Modulation classification technology is the foundation and key part of spectrum management and control. Therefore, the effectiveness of modulation classification technology in big data scenario is very important. In this paper, we consider not only the validity of the classification model under the background of big data, but also the dynamics of noise in the complex electromagnetic environment. So we construct a big dataset containing different signals under different MSNR, and use the big data to drive the deep learning model, and finally get the classification result. The proposed method can realize modulation classification only by training one model, which avoids the redundancy of model training in previous algorithms. The simulation results demonstrate the effectiveness and reliability of the proposed method. INDEX TERMS Big data, non-Gaussian noise, modulation classification, deep learning.

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Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network

Cited by 14 publications

References 33 publications

Practical AMC model based on SAE with various optimisation methods under different noise environments

Practical AMC model based on SAE with various optimisation methods under different noise environments

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

The Performance Evaluation of Big Data-Driven Modulation Classification in Complex Environment

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