Low-complexity modulation format identification scheme via graph-theory in digital coherent optical receivers

Yang, Lishan; Xu, Hengying; Chen, Bai; Yu, Xinkuo; You, Kangyong; Sun, Weibin; Zhang, Xiaoguang; Liu, Cailong

doi:10.1016/j.optcom.2021.127380

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…For an MFI scheme, the minimum required number of symbols (corresponding to the lowest OSNR required for the considered modulation format) is one of the most important factors, because it determines the response speed and computational complexity of the MFI algorithm [28,32]. The minimum required OSNR values, versus the numbers of symbols for the five modulation formats, are shown in Figure 8, wherein the symbol variation ranged from 1000 to 7000 at intervals of 1000.…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

“…The previously-reported MFI schemes for optical fiber communications were roughly classified into the following three categories: (1) data-aided schemes [9][10][11], in which additional pilot information is introduced and the computational complexity of the MFI scheme is low (at the cost of reduced spectral efficiency); (2) schemes based on Stokes space [7,8,[12][13][14][15][16][17][18][19][20][21][22][23], which are not sensitive to carrier phase noise, frequency offset or polarization mixing; (3) schemes based on signal characteristics arising from constant modulus algorithm (CMA) equalization [24][25][26][27][28][29][30][31][32][33], which are based on CMA-equalized signals and do not require any space mapping. Meanwhile, CMA can also compensate for residual chromatic dispersion (CD) and polarization mode dispersion (PMD).…”

Section: Introductionmentioning

confidence: 99%

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Low-Complexity Modulation Format Identification Based on Amplitude Histogram Distributions for Digital Coherent Receivers

et al. 2023

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In this work, a prior-training-free and low-complexity modulation format identification (MFI) scheme, based on amplitude histogram distributions, was proposed and demonstrated, both numerically and experimentally, for autonomous digital coherent receivers. In the proposed scheme, after having performed power normalization, incoming polarization division multiplexed (PDM) signals were classified into QPSK, 8QAM, 16QAM, 32QAM and 64QAM signals, according to their ratios. Ratios were defined according to specific features of their amplitude histograms. The proposed MFI scheme used only amplitude information. As such, it was insensitive to carrier phase noise. Furthermore, the proposed scheme did not require any prior information, such as optical signal-to-noise ratio (OSNR). The performance of the proposed MFI scheme was numerically verified using 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM signals. The numerical simulation results showed that the proposed scheme was able achieve a 100% correct identification rate for all five modulation formats when their OSNR values were higher than the thresholds corresponding to the 20% FEC correcting bit error rate (BER) of 2.4 × 10−2. To further explore the effectiveness of the proposed MFI scheme, proof-of-concept experiments in 28GBaud PDM-QPSK/-8QAM/-16QAM, and 21.5GBaud PDM-32QAM transmission systems were also undertaken, which showed that the proposed scheme as robust against fiber nonlinearities. To explore the scheme’s feasibility for use in practical transmission systems, the computational complexity analysis of the proposed scheme was conducted. It showed that, compared with relevant MFI schemes, the proposed MFI scheme was able to significantly reduce the computational complexity.

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Section: Numerical Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Complexity Modulation Format Identification Based on Amplitude Histogram Distributions for Digital Coherent Receivers

et al. 2023

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“…Neural network technology utilizes deep neural networks with multiple nonlinear layers to learn and extract feature information from signals, without relying on manual experience to extract signal features. MFI methods based on neural network technology have the advantages of requiring less prior knowledge, strong feature extraction capabilities, and high recognition accuracy, which have received widespread attention and research from scientific researchers [17][18][19][20] .…”

Section: Telecommunications Operators In Various Countriesmentioning

confidence: 99%

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

He,

Huang,

et al. 2024

Preprint

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In the era of the digital economy, enabling users to utilize computing power as conveniently as water and electricity is inseparable from the development of computing power networks. The carrier network of future 6G also needs to achieve deep collaboration between computing and networking. In high-speed computing power optical networks above 400G, nonlinear effects became one of the main factors limiting system transmission performance. Traditional coherent optical communication solutions are complex to implement, require high coherence and stability of light sources, which is difficult, and costly. This paper proposes a low-cost and low-complexity non-coherent solution based on the advanced modulation format Apol-CRZ-FSK to achieve 4 x 100Gbps computing power optical networks. Simulation results show that it exhibits better resistance to non-linear effects compared to traditional modulation formats such as CRZ-FSK and DQPSK, enabling longer single-span and multi-span transmission distances and superior transmission performance. Furthermore, in response to the transmission requirements and signal perception and recognition requirements in future computing optical networks, this study identifies three different modulation formats through the Inception-ResNet-v2 convolutional neural network model. When compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, the Inception-ResNet-v2 model achieved the highest accuracy rate of 99.51%, which is a 1.66% improvement over the ResNet50 model. It can provide an effective solution for low-cost, low-complexity, and high-performance signal transmission and signal recognition in the 6G era of high-speed computing power optical networks.

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“…In addition, the authors in [ 20 ] proposed an MF identification algorithm based on clustering for direct detection systems. Graph-theory can be also used for low complex MF identification [ 21 ]. Finally, MF identifications based on the Stokes space [ 22 , 23 , 24 ] are especially attractive because they can be performed before polarization demultiplexing at the Rx DSP block.…”

Section: Introductionmentioning

confidence: 99%

Accurate Low Complex Modulation Format and Symbol Rate Identification for Autonomous Lightpath Operation

Sequeira

Ruiz

Costa

et al. 2022

Sensors

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Network automation promises to reduce costs while guaranteeing the required performance; this is paramount when dealing with the forecasted highly dynamic traffic that will be generated by new 5G/6G applications. In optical networks, autonomous lightpath operation entails that the optical receiver can identify the configuration of a received optical signal without necessarily being configured from the network controller. This provides relief for the network controller from real-time operation, and it can simplify the operation in multi-domain scenarios, where an optical connection spans across more than one domain. Consequently, in this work, we propose a blind and low complex modulation format (MF) and symbol rate (SR) identification algorithm. The algorithm is based on studying the effects of decoding an optical signal with different MFs and SRs. Extensive MATLAB-based simulations have been carried out which consider a coherent wavelength division multiplexed system based on 32 and 64 quadrature amplitude modulated signals at up to 96 GBd, thus enabling bit rates of up to 800 Gb/s/channel. The results show remarkable identification accuracy in the presence of linear and nonlinear noise for a wide range of feasible configurations.

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Low-complexity modulation format identification scheme via graph-theory in digital coherent optical receivers

Cited by 5 publications

References 16 publications

Low-Complexity Modulation Format Identification Based on Amplitude Histogram Distributions for Digital Coherent Receivers

Low-Complexity Modulation Format Identification Based on Amplitude Histogram Distributions for Digital Coherent Receivers

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

Accurate Low Complex Modulation Format and Symbol Rate Identification for Autonomous Lightpath Operation

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