Modulation Format Identification Technology Based on a Searching Cluster Boundary Clustering Algorithm

Yan, Xu; Cao, Chuanbao; Zhang, Wenrui; Feng, Zhouming; Zeng, Xiaodong; Wu, Zengyan

doi:10.1109/jlt.2022.3212042

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…In recent years, there has been a proliferation of modulation format identification methods based on feature extraction. Common methods include those based on signal amplitude and phase accumulation [1][2][3], machine learning/deep learning methods [4][5][6][7], methods based on the Stokes plane or other fitting two-dimensional planes [8][9][10], methods based on clustering algorithms [11][12], and principal component analysis [13][14]. In the above method, the methods based on signal amplitude-phase accumulation or machine learning/deep learning require the collection of a great quantity of signal data samples as training data for feature calculation or dataset training in order to obtain signal features for identification.…”

Section: Introductionmentioning

confidence: 99%

Modulation format identification based on the mean-shift algorithm

Hao,

Zhang,

Zhao

et al. 2023

Fourteenth International Conference on Information Optics and Photonics (CIOP 2023)

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Section: Introductionmentioning

confidence: 99%

Modulation format identification based on the mean-shift algorithm

Hao,

Zhang,

Zhao

et al. 2023

Fourteenth International Conference on Information Optics and Photonics (CIOP 2023)

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Transfer learning-assisted modulation classification and OSNR estimation using constellation diagrams

Jha,

Mishra

2024

J. Opt.

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A cost-effective deep transfer learning (TL) learned with characteristics derived from the constellation diagram is proposed for executing modulation classification and optical signal-to-noise ratio (OSNR) estimation for the future generation of elastic optical networks. The information acquired with the ImageNet dataset is transferred using pre-trained TL versions including VGG19, ResNet152V2, InceptionV3, and ResNet50 to identify various modulation formats and their corresponding OSNR. Adam optimizer is employed to ascertain the appropriate settings for the hyperparameters. Numerical results demonstrate that the proposed VGG19 method achieves the highest level of accuracy (100%) in the recognition of various modulation forms. To fulfill the requirements of actual use, OSNR monitoring is also explored, with a total precision equal to 95.2%. Furthermore, a thorough investigation of the impact of training data dimensions, on TL performance is conducted. The outcomes of the proposed method demonstrate that the suggested TL-based algorithms are more accurate and need much less training and testing time than non-TL approaches.

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High-Speed Optical Signal Identification Scheme Based on Multidimensional Fractal

Zhang,

Huang

et al. 2024

2024 22nd International Conference on Optical Communications and Networks (ICOCN)

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Modulation Format Identification Technology Based on a Searching Cluster Boundary Clustering Algorithm

Cited by 3 publications

References 15 publications

Modulation format identification based on the mean-shift algorithm

Modulation format identification based on the mean-shift algorithm

Transfer learning-assisted modulation classification and OSNR estimation using constellation diagrams

High-Speed Optical Signal Identification Scheme Based on Multidimensional Fractal

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