Classification of Spectrally Efficient Constant Envelope Modulations Based on Radial Basis Function Network and Deep Learning

Shah, Maqsood Hussain; Dang, Xiaoyu

doi:10.1109/lcomm.2019.2927348

“…For AIpowered communications, DL is being exploited to address many challenging design tasks including network traffic control [24] and intelligent resource allocation [88]. For AMC, DNN [89]- [91] has been recommended to replace traditional classifiers for learning statistical features. For example, two sparse autoencoder-based DNNs were developed [89], [90] to improve the accuracy of high-order and intraclass digital modulations.…”

Section: B State-of-the-art Amc Methodsmentioning

confidence: 99%

“…For AMC, DNN [89]- [91] has been recommended to replace traditional classifiers for learning statistical features. For example, two sparse autoencoder-based DNNs were developed [89], [90] to improve the accuracy of high-order and intraclass digital modulations. 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.…”

Section: B State-of-the-art Amc Methodsmentioning

confidence: 99%

Intelligent Radio Signal Processing: A Survey

Pham

¹

,

Nguyen

²

,

Huynh‐The

³

et al. 2020

Preprint

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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.

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“…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%

“…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. 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.…”

Section: ) Innovative Dl-based Amc Approachesmentioning

confidence: 99%

Intelligent Radio Signal Processing: A Survey

Pham

¹

,

Nguyen

²

,

Huynh‐The

³

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

“…The modulation classification procedure takes place after preprocessing prior to demodulation of the incoming signal [1][2][3][4][5][6][7]. The likelihood-based techniques are based on the signal received probability, while the feature-based methods contain two modules: function extraction and structure of the classifier [8][9][10][11][12][13][14][15]. Random graphs (RGs) have been used to model a multitude of random-like networks, including the unpredictable growth of the Internet's web graph, page ranking, and neural networks.…”

Section: Introductionmentioning

confidence: 99%

Random Graph-Based M-QAM Classification for MIMO Systems

Sarfraz

¹

,

Alam

²

,

Ghauri

³

et al. 2022

Wireless Communications and Mobile Computing

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Automatic modulation classification (AMC) has been identified to perform a key role to realize technologies such as cognitive radio, dynamic spectrum management, and interference identification that are arguably pivotal to practical SG communication networks. Random graphs (RGs) have been used to better understand graph behavior and to tackle combinatorial challenges in general. In this research article, a novel modulation classifier is presented to recognize M-Quadrature Amplitude Modulation (QAM) signals using random graph theory. The proposed method demonstrates improved recognition rates for multiple-input multiple-output (MIMO) and single-input single-output (SISO) systems. The proposed method has the advantage of not requiring channel/signal to noise ratio estimate or timing/frequency offset correction. Undirected RGs are constructed based on features, which are extracted by taking sparse Fourier transform (SFT) of the received signal. This method is based on the graph representation of the SFT of the 2nd, 4th, and 8th power of the received signal. The simulation results are also compared to existing state-of-the-art methodologies, revealing that the suggested methodology is superior.

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Classification of Spectrally Efficient Constant Envelope Modulations Based on Radial Basis Function Network and Deep Learning

Cited by 13 publications

References 18 publications

Intelligent Radio Signal Processing: A Survey

Intelligent Radio Signal Processing: A Survey

Intelligent Radio Signal Processing: A Survey

Random Graph-Based M-QAM Classification for MIMO Systems

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