128-QAM dual-polarization chaotic long-haul system performance evaluation

Qamar, Farhan; Islam, Md. Ariful; Shahzadi, Romana; Ali, Syed Zafar; Ali, Mudassar

doi:10.1515/joc-2020-0092

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…In addition, the signal had four possible modulation formats: 16 / 32 / 64 / 128-QAM. We tested modulation formats up to 128QAM since recent publications showed performance analysis for these modulation formats using simulation as well as experiment setups [37][38][39]. We considered the following two test cases:…”

Section: A Numerical Setup and Neural Network Modelmentioning

confidence: 99%

Transfer Learning for Neural Networks-Based Equalizers in Coherent Optical Systems

Freire

Abode

Prilepsky

et al. 2021

J. Lightwave Technol.

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In this work, we address the question of the adaptability of artificial neural networks (NNs) used for impairments mitigation in optical transmission systems. We demonstrate that by using well-developed techniques based on the concept of transfer learning, we can efficaciously retrain NN-based equalizers to adapt to the changes in the transmission system, using just a fraction (down to 1%) of the initial training data or epochs. We evaluate the capability of transfer learning to adapt the NN to changes in the launch power, modulation format, symbol rate, or even fiber plants (different fiber types and lengths). The numerical examples utilize the recently introduced NN equalizer consisting of a convolutional layer coupled with bi-directional long-short term memory (biLSTM) recurrent NN element. Our analysis focuses on long-haul coherent optical transmission systems for two types of fibers: the standard single-mode fiber (SSMF) and the TrueWave Classic (TWC) fiber. We underline the specific peculiarities that occur when transferring the learning in coherent optical communication systems and draw the limits for the transfer learning efficiency. Our results demonstrate the effectiveness of transfer learning for the fast adaptation of NN architectures to different transmission regimes and scenarios, paving the way for engineering flexible and universal solutions for nonlinearity mitigation.

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Section: A Numerical Setup and Neural Network Modelmentioning

confidence: 99%

Transfer Learning for Neural Networks-Based Equalizers in Coherent Optical Systems

Freire

Abode

Prilepsky

et al. 2021

J. Lightwave Technol.

View full text Add to dashboard Cite

show abstract

Enhancing performance of optical chaotic-based secure fiber-optic communication system

et al. 2023

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Enhancing network security with hybrid feedback systems in chaotic optical communication

Ashraf,

Singh,

Pandian

et al. 2024

Sci Rep

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This paper presents a pioneering approach to bolstering network security and privacy by implementing chaotic optical communication with a hybrid optical feedback system (HOFS). The current baseline methods in network security are often less feasible for hybrid feedback systems, including limited robustness, compromised security, and synchronization challenges. Therefore, this paper proposes a hybrid approach to address these shortcomings by integrating the HOFS into chaotic optical communication systems (HOFS-COCS) to overcome the baseline challenges. This paper aims to improve network security while significantly maintaining efficient communication channels. Moreover, We designed two algorithms, one for chaotic maps generation and another for text encryption and decryption, to improve security in the hybrid feedback system. Our findings demonstrate through rigorous experimentation and analysis that the proposed (HOFS-COCS) method significantly improves network security by enabling reliable chaos generation, synchronization, and secure message transmission in chaotic optical communication systems. This research represents a significant advancement towards enhanced secrecy and synchronization in chaotic optical communication systems, promising a paradigm shift in network security protocols.

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128-QAM dual-polarization chaotic long-haul system performance evaluation

Cited by 4 publications

References 17 publications

Transfer Learning for Neural Networks-Based Equalizers in Coherent Optical Systems

Transfer Learning for Neural Networks-Based Equalizers in Coherent Optical Systems

Enhancing performance of optical chaotic-based secure fiber-optic communication system

Enhancing network security with hybrid feedback systems in chaotic optical communication

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