An m -ary adaptive demodulator based on machine learning for light beams carrying orbital angular momentums (OAMs) over free-space turbulence channels is proposed and demonstrated. Benefiting from ...natural advantages in the image recognition, convolutional neural network (CNN) is selected to construct the adaptive demodulator. Without extra space light modulators and digital signal processing at the reception, the adaptive demodulator transforms the sequence of intensity patterns of received Laguerre-Gaussian beams carrying different OAM modes into initial signals efficiently. As comparison, K-nearest neighbor (KNN), naive Bayes classifier (NBC), and back-propagation artificial neural network (BP-ANN) are also studied. Furthermore, the demodulating accuracy of 4-, 8-, and 16-ary OAM is investigated with the comprehensive consideration of the atmospheric turbulence, OAM mode spacing, and transmission distance. The simulation results show that the demodulating error rate (DER) of CNN outperforms KNN, NBC, and BP-ANN, especially under stronger turbulence and longer distance. The DER of CNN is ~0.86% for the 1000-m 8-OAM system under strong turbulence, ~30 % less than those of KNN, NBC, and BP-ANN.
In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine ...(SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.
A data-driven fiber channel modeling method based on deep learning (DL) is introduced in an optical communication system. In this study, bidirectional long short-term memory (BiLSTM) is selected from ...a diverse range of DL algorithms to perform fiber channel modeling for on-off keying and pulse amplitude modulation 4 signals. Compared with the conventional model-driven split-step Fourier (SSF)-based method, the proposed method yields similar results based on the comprehensive comparison of multiple characteristics associated with the generated optical signals, including the optical amplitude and phase waveforms in the time domain, optical spectrum components in the frequency domain, and eye diagrams after detection in the electrical domain. Additionally, the effects of multiple factors on the modeled fiber channel have also be investigated, including fiber length, fiber nonlinearity, dispersion, data pattern, pulse shaping, and sample rate. The satisfactory fitting results and acceptable mean square errors indicate that the approximate transfer function of the fiber channel is learned by the BiLSTM. Moreover, compared with repetitive iteration SSF, the computing time is significantly reduced by the BiLSTM owing to its independence on fiber length and insensitivity to data size and launch power. Our aim is to demonstrate the BiLSTM is comparable with the conventional model-driven SSF-based method for direct-detection optical fiber system. We think the proposed method could be a supplementary technique that can be used for the existing simulation system and could also be a potential option for future simulation methods.
Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses ...on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.
A powerful machine learning detector based on the k-nearest neighbors (KNN) algorithm is proposed to overcome system impairments. The zero-dispersion link (ZDL), dispersion managed link (DML), and ...dispersion unmanaged link (DUL) are considered. Meanwhile, an improved algorithm, the distance-weight KNN, is introduced, which outperforms the conventional maximum likelihood-post compensation approach. The numerical results show that KNN is feasible for overcoming various impairments, especially for non-Gaussian symmetric noise, such as laser phase noise and nonlinear phase noise in the ZDL or DML.
A novel low-complexity and nonlinearity-tolerant modulation format identification (MFI) using random forest (RF) is proposed for the flexible coherent receivers (FCRs). The RF-based MFI takes the ...advantage of distinct features from the amplitude histograms (AHs) and the swarm intelligence of RF to significantly reduce the computational complexity compared with the deep learning-based method. Both numerical simulation and experiments are conducted. The simulation results of polarization multiplexed (PM) 4/8/16/32/64 quadrature amplitude modulation (QAM) wavelength division multiplexing (WDM) systems demonstrate its feasibility and identification performance. In the presence of nonlinear effects, with lower complexity than three other machine learning algorithms (k-nearest neighbors, support vector machine, and deep neural networks), the RF-based MFI can obtain 100% accuracy at the OSNR values greater than or equal to the respective soft decision forward error correction (SD-FEC) threshold. The identification accuracy versus total launch power is experimentally investigated in PM-4/16/32QAM WDM coherent systems. In the case of obvious nonlinear impairments, 100% accuracy can also be achieved above the OSNR corresponding to the SD-FEC threshold. The superiority of the proposed MFI method makes it highly desirable for applications in modulation format-adaptive FCRs.
In the coherent long-reach passive optical networks (LRPON), it is crucial to propose cost-effective digital signal processing (DSP) technologies to reduce the overall complexity and power ...consumption. This paper has proposed a low-complexity chromatic dispersion (CD) estimation scheme based on deep neural networks (DNN) and the error vector magnitude (EVM). To add comparisons, the performances of CD estimation schemes using other two well-known machine learning algorithms including the k-nearest neighbor (KNN) and the decision tree (DT) have also been investigated. The simulation results show that the proposed CD estimation scheme is effective in the coherent LRPON with the quadrature phase shift keying (QPSK) and 16-ary quadrature amplitude modulation (QAM) systems at 14Gbaud rate, 28Gbaud rate and 56Gbaud rate. The comprehensive performances of the DNN outperform those of the KNN and the DT. The mean estimation error of the DNN is less than 20ps/nm within the 100 km access distance in the 28Gbaud QPSK/16QAM systems. What's more, compared with classical methods using the CD scanning and frequent domain equalizers (FDE), the computation complexity of the proposed CD estimation scheme based on the DNN-EVM has been respectively reduced by 72.3 times, 86.7 times and 2.8 times about the amount of multipliers, adders and comparators.
Visible light communication (VLC) is considered an important complementary technology for extremely high sixth-generation (6G) data transmission and has become part of a hybrid 6G indoor network ...architecture with an ultradense deployment of VLC access points (APs) that presents severe challenges to user mobility. An adaptive handover mechanism, which includes a seamless handover protocol and a selection algorithm optimized with a deep reinforcement learning (DRL) method, is proposed to overcome these challenges. Experimental simulation results reveal that the average downlink data rate with the proposed algorithm is up to 48% better than those with traditional RL algorithms and that this algorithm also outperforms the deep Q-network (DQN), Sarsa and Q-learning algorithms by 8%, 13% and 13%, respectively.
In this paper, a novel joint symbol rate-modulation format identification (SR-MFI) and optical signal-to-noise ratio (OSNR) estimation scheme using the low-bandwidth coherent detecting and random ...forest (RF)-based ensemble learning is proposed for intermediate nodes in the flexible dense wavelength division multiplexing (F-DWDM) networks. By leveraging low-bandwidth coherent detecting with small bulk wavelength scanning, no chromatic dispersion compensation and low-complexity RF, the proposed scheme could serve as a reduced-complexity and cost-effective option to realize joint SR-MFI and OSNR estimation at intermediate nodes in F-DWDM networks. To verify the feasibility of the proposed scheme, the comprehensive simulations of 8/16 GBaud polarization division multiplexing (PDM)-4/16/32/64 quadrature amplitude modulation (QAM) systems are conducted. The simulation results show that the identification accuracy of SR-MFI reaches 100% and the mean absolute error of OSNR estimation is within 1 dB. Moreover, the proposed monitoring scheme is verified by 8/16 GBaud PDM-4/16/32QAM coherent transmission experiments.
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