Adaptive nonsymmetrical demodulation based on machine learning to mitigate time-varying impairments

Torres, Jorge López; Chiuchiarelli, Andrea; Thomas, Varghese; Ralph, Stephen E.; Soto, Ana Maria Cardenas; González, Neil Guerrero

doi:10.1109/avfop.2016.7789906

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…In unsupervised learning [1], an agent learns patterns from the input even though no explicit output is supplied. For instance, clustering and principal component analysis methods, which belong to this type of learning, have been used for optical performance monitoring, modulation format recognition and impairment mitigation [40,41,42]. Finally, in reinforcement learning [43] an agent learns an optimal (or nearly optimal) policy from a series of reinforcements (rewards) or punishments received from its interaction with the environment.…”

Section: An Overview Of Ai and Related Techniquesmentioning

confidence: 99%

“…K-nearest neighbors [57]: proposes an algorithm that learns the link properties and generates the nonlinear decision boundaries for maximizing transmission distance and improving nolinear tolerance. Clustering k-means [42]: proposes a technique to mitigate the effect of time-varying impairments, e.g., phase noise. Nonlinear support vector machines and Newton method [58]: uses Newton-method (N-SVM) to reduce inter-subcarrier nonlinear crosstalk effects.…”

Section: Receivers Nonlinearity Mitigationmentioning

confidence: 99%

“…In this way, maximum transmission distance and nonlinear tolerance improvements are demonstrated in a 16 QAM coherent transmission system. Following the same line of study, Torres et al [42] propose a non-symmetric demodulation technique for receivers equipped with DSP based on clustering (using k-means algorithm), which mitigates the effect of time-varying impairments such as imbalance of in-phase and quadrature signals (IQ imbalance), bias drift and phase noise. This machine learningbased demodulator is computationally highly efficient and also transparent with respect to the nonlinearity source.…”

Section: Receivers and Mitigation Of Nonlinearitiesmentioning

confidence: 99%

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Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

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Section: An Overview Of Ai and Related Techniquesmentioning

confidence: 99%

Section: Receivers Nonlinearity Mitigationmentioning

confidence: 99%

Section: Receivers and Mitigation Of Nonlinearitiesmentioning

confidence: 99%

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Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

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Durán

et al. 2018

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338

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View full text Add to dashboard Cite

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A Reinforcement Learning-Based QAM/PSK Symbol Synchronizer

et al. 2019

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Machine Learning (ML) based on supervised and unsupervised learning models has been recently applied in the telecommunication field. However, such techniques rely on application-specific large datasets and the performance deteriorates if the statistics of the inference data changes over time. Reinforcement Learning (RL) is a solution to these issues because it is able to adapt its behavior to the changing statistics of the input data. In this work, we propose the design of an RL Agent able to learn the behavior of a Timing Recovery Loop (TRL) through the Q-Learning algorithm. The Agent is compatible with popular PSK and QAM formats. We validated the RL synchronizer by comparing it to the Mueller and Müller TRL in terms of Modulation Error Ratio (MER) in a noisy channel scenario. The results show a good trade-off in terms of MER performance. The RL based synchronizer loses less than 1 dB of MER with respect to the conventional one but it is able to adapt its behavior to different modulation formats without the need of any tuning for the system parameters.

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Adaptive nonsymmetrical demodulation based on machine learning to mitigate time-varying impairments

Cited by 2 publications

References 4 publications

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

A Reinforcement Learning-Based QAM/PSK Symbol Synchronizer

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