Journal ArticleDOI
Blind Signal PSK/QAM Recognition Using Clustering Analysis of Constellation Signature in Flat Fading Channel
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TLDR
Results show that the proposed algorithm outperforms some existing classifiers and offers lower computational complexity compared to algorithms based on subtractive clustering.Abstract:
A novel method based on constellation structure is proposed to identify PSK and QAM modulation of different orders, in the slow and flat fading channel. The proposed method does not require training for threshold optimization and considers carrier frequency, symbol rate, and phase offset unknown. The symbol rate is estimated using the spectrum of the instantaneous phase of the complex baseband signal. Carrier frequency offset (CFO) is estimated and corrected from the downconverted signal and downsampled to the estimated symbol rate for extraction of constellation points. The phase offset is determined based on the symmetrical structure of constellation. The features extracted using k-medoids are used for classification of the final modulation scheme. Results show that the proposed algorithm outperforms some existing classifiers and offers lower computational complexity compared to algorithms based on subtractive clustering.read more
Citations
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Journal ArticleDOI
Automatic Modulation Classification Based on Constellation Density Using Deep Learning
TL;DR: In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM to demonstrate better performance compared to many existing classifiers in the literature.
Journal ArticleDOI
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey
TL;DR: In this article, the authors 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.
Proceedings ArticleDOI
Learning Constellation Map with Deep CNN for Accurate Modulation Recognition
TL;DR: In this paper, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image, where several grouped and asymmetric CNN layers in each block are organized by a flow-in-flow structure for feature enrichment.
Journal ArticleDOI
Autonomous and Energy Efficient Lightpath Operation Based on Digital Subcarrier Multiplexing
Luis Velasco,Sima Barzegar,Diogo Gonçalo Sequeira,Alessio Ferrari,Nelson Costa,Vittorio Curri,Joao Pedro,Antonio Napoli,Marc Ruiz +8 more
TL;DR: This paper presents several solutions enabling the autonomous DSCM operation, including: i) SC quality of transmission estimation; ii) autonomous SC operation at the transmitter side and blind SC configuration recognition at the receiver side; and iii) intent-based capacity management implemented through Reinforcement Learning.
Journal ArticleDOI
Soft-Information Assisted Modulation Recognition for Reconfigurable Radios
Mohamed Marey,Hala Mostafa +1 more
TL;DR: For the first time in the literature, it is shown how the output of a channel decoder can be exploited to iteratively estimate the maximum-likelihood value of the transmit modulation format.
References
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Journal ArticleDOI
Survey of automatic modulation classification techniques: classical approaches and new trends
TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
Journal ArticleDOI
Automatic Modulation Classification Using Combination of Genetic Programming and KNN
TL;DR: This paper explores the use of Genetic Programming in combination with K-nearest neighbor (KNN) for AMC and demonstrates that the proposed method provides better classification performance compared to other recent methods.
Journal ArticleDOI
Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test
Fanggang Wang,Xiaodong Wang +1 more
TL;DR: Compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.
Journal ArticleDOI
Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers
TL;DR: A Hierarchical Polynomial (HP) classifier is proposed to automatically classify M-PSK and M-QAM signals in Additive White Gaussian Noise and slow flat fading environments and shows an overall improvement in the probability of correct classification that reaches 100% using only 512 received symbols at 20 dB.
Journal ArticleDOI
A Faster Maximum-Likelihood Modulation Classification in Flat Fading Non-Gaussian Channels
TL;DR: The numerical results show that the proposed squared iterative method with parameter checking can accelerate the convergence rate of ECM algorithm, and AMC based on the proposed method is faster than that based on ECM, while the accuracy of the former shows nearly no loss compared with that of the latter.