Novel Feature Selection Method Using Bhattacharyya Distance for Neural Networks Based Automatic Modulation Classification

Shah, Maqsood Hussain; Dang, Xiaoyu

doi:10.1109/lsp.2019.2957924

Cited by 22 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combined vector is then fed into both CNN and LSTM classifiers, namely CNN-IQFOC and LSTM-IQFOC, respectively. In [90], the impacts of features selection for AMR were investigated. Moreover, a novel method for selecting the most effective and distinctive features from a larger set of features was proposed.…”

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

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: 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

“…For AI-powered communications, DL is being exploited to address many challenging design tasks including network traffic control [25] and intelligent resource allocation [89]. For AMC, DNN [90]- [92] has been recommended to replace traditional classifiers for learning statistical features. For example, two sparse autoencoderbased DNNs were developed [90], [91] to improve the accuracy of high-order and intraclass digital modulations.…”

Section: ) Innovative Dl-based Amc Approachesmentioning

confidence: 99%

“…Although their performance is slightly higher than that of LSVM and approximately maximum-likelihood classifiers, they are computationally more complex because of the requirement to compute a large number of neurons in hidden layers. Selection of the most relevant HOC features for learning a sparse autoencoder DNN [92] makes it possible to substantially reduce the overall complexity of the classifier without performance loss. LSTM, an advanced architecture of RNN that exploits the long-term dependencies between temporal attributes in sequential data, was further studied for modulation classification [93].…”

Section: ) Innovative Dl-based Amc Approachesmentioning

confidence: 99%

Intelligent Radio Signal Processing: A Survey

et al. 2021

View full text Add to dashboard Cite

Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

show abstract

“…Identification of PSK and QAM signals have been investigated in [16], [27], [34], [35]. The work in [16] focused on the modulation classification of only three types of signals (i.e., QPSK, 16QAM and 64QAM).…”

Section: Introductionmentioning

confidence: 99%

“…An outage or any disconnection in these nodes will cause a failure in the entire communication system, 6) the scheme assumes that SNR is known. A Similar modulation family to [27], has been deemed in the work [35] to handle AMR problem using their features-based technique.…”

Section: Introductionmentioning

confidence: 99%

Dual-Determination of Modulation Types and Signal-to-Noise Ratios Using 2D-ASIQH Features for Next Generation of Wireless Communication Systems

et al. 2021

View full text Add to dashboard Cite

In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes' histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0-35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0-35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.

show abstract

Novel Feature Selection Method Using Bhattacharyya Distance for Neural Networks Based Automatic Modulation Classification

Cited by 22 publications

References 14 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

Intelligent Radio Signal Processing: A Survey

Dual-Determination of Modulation Types and Signal-to-Noise Ratios Using 2D-ASIQH Features for Next Generation of Wireless Communication Systems

Contact Info

Product

Resources

About