Robust weighted K-means clustering algorithm for a probabilistic-shaped 64QAM coherent optical communication system

Wang, Xishuo; Zhang, Qi; Xin, Xiangjun; Tian, Qinghua; Tian, Feng; Pan, Xiaolong; Wang, Yongjun; Yang, Leijing

doi:10.1364/oe.27.037601

Cited by 25 publications

(11 citation statements)

References 23 publications

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“…In the Ref. [19], a weighted K-means scheme for a PS 64QAM signal was proposed in order to locate the decision points more accurately and enhance the robustness of clustering algorithm. In addition, the combined benefit of artificial neural network-based nonlinearity compensation and probabilistic shaping was experimentally demonstrated in the Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Nonlinear Equalizer for Probabilistic Shaping 64-QAM Based on Constellation Segmentation and Support Vector Machine

Wang

et al. 2022

Electronics

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The probability distribution of probabilistic shaping 64 quadrature amplitude modulation (PS-64QAM) is uneven. The traditional M-ary support vector machine (SVM) algorithm is incompatible with the data set with uneven distribution. In order to solve the problem, we propose a novel nonlinear equalizer (NLE) for PS-64QAM based on constellation segmentation (CS) and SVM, called CS M-ary SVM NLE. The performance of CS M-ary SVM NLE has been demonstrated in a 120 Gb/s PS-64QAM coherent optical communication system. The experimental results show that after employing the proposed scheme, the launched optical power dynamic range (LOPDR) of PS-64QAM can be increased by 1.6 dBm compared with the situation without NLE. In addition, aided by the proposed scheme, the LOPDR of PS-64QAM is increased by 0.6 dBm than M-ary SVM NLE. Compared with employing M-ary SVM NLE and without employing NLE, when employing the proposed scheme, the Q factor is improved about 0.50 dB and 0.96 dB, respectively. The number of support vectors (SVs) and CPU running time for both NLE schemes are collected to measure the complexity of the two NLE schemes. The results show that the complexity of the proposed scheme is lower than that of the M-ary SVM scheme under the entire measured launched optical power range from −5 dBm to +5 dBm.

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Section: Introductionmentioning

confidence: 99%

A Novel Nonlinear Equalizer for Probabilistic Shaping 64-QAM Based on Constellation Segmentation and Support Vector Machine

Wang

et al. 2022

Electronics

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“…Breaking the original survival strategy, paying attention to operating enterprises with new strategic thinking, and constantly identifying and cultivating strategic innovation ability are all things that businesses must do. Only in this way will we be able to effectively implement strategic innovation and, as a result, create a continuous competitive advantage, allowing businesses to grow sustainably in the market [ 7 ]. When a company discovers that its strategy is no longer generating value for customers, it has the ability to reidentify, acquire, and integrate the industry's internal and external environmental resources.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm

Shang

2022

Computational Intelligence and Neuroscience

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Improving enterprises’ independent innovation capability is critical to improving their competitive strength, industries’ independent innovation capability, industries’ international competitiveness, and countries’ independent innovation capability, as well as building an innovative country. It is now the era of strategic innovation. Many growing businesses are focused on strategic innovation, but they overlook the issue of ensuring that strategic innovation is implemented. Management and innovation are perennial themes in the long-term development of businesses. The most pressing issue in enterprises’ strategic innovation activities is how to combine their strategic direction with their superior ability and choose a strategic innovation path that is appropriate for their development. This paper uses data mining theory to establish the K-means clustering algorithm to identify the best strategic orientation of enterprise innovation strategic direction selection based on the existing advantages and capabilities of enterprises based on the analysis of the direction of enterprise strategic innovation.

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“…Due to the increase in space engineering projects and tasks, the complexity of modulation formats in satellite laser communication systems is increasing in order to meet the needs of different users and services and make full use of channel capacity [1][2][3][4][5][6][7]. To expand transmission capacity, the Optical Frequency Division Multiplexing (OFDM) technique has attracted attention, and the OFDM system was found to be highly resistant to multipath interference and spectrally selective fading as well as having good BER performance [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Carrier Signal Recognition Method Based on Multi-Feature Input and Hybrid Training Neural Network

Cui

Zhang

et al. 2022

Electronics

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In order to achieve automatic identification of modulation formats in orthogonal frequency division multiplexing (OFDM) subcarrier signals, a recognition method based on multiple feature inputs and a Hybrid Training Neural Network (HTNN) is proposed, in which an HTNN structure is designed to obtain high-order statistical correlation features and constellations of OFDM subcarriers. The recognition performance of the proposed method in free space channel transmission and atmospheric time-varying channel transmission is studied by simulation. Simulation results show that the overall identification accuracy of the recognition model based on the proposed method exceeded 93.37% in the wide Signal-to-Noise Ratio (SNR) range of the free space channel. With an SNR higher than 7.5 dB, identification accuracy performance of the learning model culminated, achieving 100% identification accuracy of OFDM subcarrier signals. Under weak turbulent atmospheric and time-varying channel conditions, the overall identification accuracy curve tended to increase as SNR increased and stabilized at more than 95%. Compared with the two comparison methods, the proposed method reduced the sensitivity to channel variations and improved the convergence speed on the basis of the guaranteed identification accuracy, and enabled reliable identification of OFDM subcarrier signals in a wide SNR range.

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Robust weighted K-means clustering algorithm for a probabilistic-shaped 64QAM coherent optical communication system

Cited by 25 publications

References 23 publications

A Novel Nonlinear Equalizer for Probabilistic Shaping 64-QAM Based on Constellation Segmentation and Support Vector Machine

A Novel Nonlinear Equalizer for Probabilistic Shaping 64-QAM Based on Constellation Segmentation and Support Vector Machine

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm

Multi-Carrier Signal Recognition Method Based on Multi-Feature Input and Hybrid Training Neural Network

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