Optical Impairment Compensation in Fiber Communication Systems Based on Artificial Intelligence: A Comprehensive Survey

AbstractIn this paper, the performance of a novel multilevel signaling based on Manchester code namely four-level Manchester Coding (4-MC) technique is investigated for next generation high-speed optical fiber communication links. The performance of 4-MC is studied and compared with conventional Manchester modulation and four-level pulse amplitude modulation (4-PAM) formats in terms of receiver sensitivity, spectral efficiency and dispersion tolerance at the bit rate of 40 Gb/s. The bit error rate (BER) calculation model for the proposed multilevel scheme has also been developed. The calculated receiver sensitivity and the chromatic dispersion tolerance at the BER of 10–9 of the proposed scheme are −22 dBm and 67.5 ps/nm, respectively. It is observed that, 4-MC scheme is superior in comparison to 4-PAM by 3.5 dB in terms of receiver sensitivity in back-to-back scenario. Therefore, the proposed scheme can be considered as an alternative to current 4-PAM system.

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Progress towards heterodyne-type single-mode fiber communication systems

IEEE Journal of Quantum Electronics ◽

10.1109/jqe.1981.1071235 ◽

1981 ◽

Vol 17 (6) ◽

pp. 897-906 ◽

Cited By ~ 75

Author(s):

F. Favre ◽

L. Jeunhomme ◽

I. Joindot ◽

M. Monerie ◽

J.C. Simon

Keyword(s):

Communication Systems ◽

Single Mode ◽

Single Mode Fiber ◽

Fiber Communication

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Optical fiber communication systems

Microelectronics Journal ◽

10.1016/s0026-2692(97)80958-5 ◽

1997 ◽

Vol 28 (5) ◽

pp. 601-602

Author(s):

M.S. Harris

Keyword(s):

Optical Fiber ◽

Communication Systems ◽

Optical Fiber Communication ◽

Fiber Communication

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Long-haul coherent optical fiber communication systems using optical amplifiers

Journal of Lightwave Technology ◽

10.1109/50.65883 ◽

1991 ◽

Vol 9 (2) ◽

pp. 251-260 ◽

Cited By ~ 29

Author(s):

S. Ryu ◽

S. Yamamoto ◽

H. Taga ◽

N. Edagawa ◽

Y. Yoshida ◽

...

Keyword(s):

Optical Fiber ◽

Communication Systems ◽

Optical Amplifiers ◽

Optical Fiber Communication ◽

Fiber Communication

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2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)

10.1109/icabcd51485.2021 ◽

2021 ◽

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Communication Systems ◽

Data Communication ◽

International Conference ◽

Big Data Computing

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Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

International Journal of Innovation in the Digital Economy ◽

10.4018/ijide.2021100103 ◽

2021 ◽

Vol 12 (4) ◽

pp. 35-42

Author(s):

Thomas Alan Woolman ◽

Philip Lee

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Communication Systems ◽

Digital Communication ◽

The United States ◽

Learning Technologies ◽

Technological Unemployment ◽

Current State ◽

Challenges And Opportunities ◽

Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

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A Hybrid Approach to Call Admission Control in 5G Networks

Advances in Fuzzy Systems ◽

10.1155/2018/2535127 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Mohammed Al-Maitah ◽

Olena O. Semenova ◽

Andriy O. Semenov ◽

Pavel I. Kulakov ◽

Volodymyr Yu. Kucheruk

Keyword(s):

Artificial Intelligence ◽

Quality Of Service ◽

Admission Control ◽

Mobile Communications ◽

Communication Systems ◽

Call Admission Control ◽

Hybrid Approach ◽

5G Networks ◽

Call Admission

Artificial intelligence is employed for solving complex scientific, technical, and practical problems. Such artificial intelligence techniques as neural networks, fuzzy systems, and genetic and evolutionary algorithms are widely used for communication systems management, optimization, and prediction. Artificial intelligence approach provides optimized results in a challenging task of call admission control, handover, routing, and traffic prediction in cellular networks. 5G mobile communications are designed as heterogeneous networks, whose important requirement is accommodating great numbers of users and the quality of service satisfaction. Call admission control plays a significant role in providing the desired quality of service. An effective call admission control algorithm is needed for optimizing the cellular network system. Many call admission control schemes have been proposed. The paper proposes a methodology for developing a genetic neurofuzzy controller for call admission in 5G networks. Performance of the proposed admission control is evaluated through computer simulation.

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