Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach

Muhammad Usman Hadi; Abdul Basit; Kiran Khurshid

doi:10.1109/INMIC50486.2020.9318206

Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach

Muhammad Usman Hadi
, Abdul Basit
, Kiran Khurshid

Faculty Of Computing, Eng. & Built Env.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

Abstract

Machine learning (ML) methodologies gave an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 40-Gb/s 128-quadrature amplitude modulation (QAM) signal based Radio over Fiber (RoF) system is experimentally evaluated for 70 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator, input signal power and noise due to amplified spontaneous emission. By employing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

Original language	English
Title of host publication	Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728198934
DOIs	https://doi.org/10.1109/INMIC50486.2020.9318206
Publication status	Published (in print/issue) - 5 Nov 2020
Event	23rd IEEE International Multi-Topic Conference, INMIC 2020 - Bahawalpur, Pakistan Duration: 5 Nov 2020 → 7 Nov 2020

Publication series

Name	Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020

Conference

Conference	23rd IEEE International Multi-Topic Conference, INMIC 2020
Country/Territory	Pakistan
City	Bahawalpur
Period	5/11/20 → 7/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Nonlinearity Mitigation
Radio over Fiber
Support Vector Machine

Access to Document

10.1109/INMIC50486.2020.9318206

Cite this

Hadi, M. U., Basit, A., & Khurshid, K. (2020). Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach. In Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020 Article 9318206 (Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INMIC50486.2020.9318206

Hadi, Muhammad Usman ; Basit, Abdul ; Khurshid, Kiran. / Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach. Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020).

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title = "Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach",

abstract = "Machine learning (ML) methodologies gave an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 40-Gb/s 128-quadrature amplitude modulation (QAM) signal based Radio over Fiber (RoF) system is experimentally evaluated for 70 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator, input signal power and noise due to amplified spontaneous emission. By employing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.",

keywords = "Nonlinearity Mitigation, Radio over Fiber, Support Vector Machine",

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year = "2020",

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language = "English",

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Hadi, MU, Basit, A & Khurshid, K 2020, Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach. in Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020., 9318206, Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020, Institute of Electrical and Electronics Engineers Inc., 23rd IEEE International Multi-Topic Conference, INMIC 2020, Bahawalpur, Pakistan, 5/11/20. https://doi.org/10.1109/INMIC50486.2020.9318206

Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach. / Hadi, Muhammad Usman; Basit, Abdul; Khurshid, Kiran.
Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9318206 (Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach

AU - Hadi, Muhammad Usman

AU - Basit, Abdul

AU - Khurshid, Kiran

PY - 2020/11/5

Y1 - 2020/11/5

N2 - Machine learning (ML) methodologies gave an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 40-Gb/s 128-quadrature amplitude modulation (QAM) signal based Radio over Fiber (RoF) system is experimentally evaluated for 70 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator, input signal power and noise due to amplified spontaneous emission. By employing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

AB - Machine learning (ML) methodologies gave an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 40-Gb/s 128-quadrature amplitude modulation (QAM) signal based Radio over Fiber (RoF) system is experimentally evaluated for 70 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator, input signal power and noise due to amplified spontaneous emission. By employing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

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DO - 10.1109/INMIC50486.2020.9318206

M3 - Conference contribution

AN - SCOPUS:85099473476

T3 - Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020

BT - Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Hadi MU, Basit A, Khurshid K. Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach. In Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9318206. (Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020). doi: 10.1109/INMIC50486.2020.9318206

Nonlinearities Mitigation in Radio over Fiber Links for beyond 5G C-RAN Applications using Support Vector Machine Approach

Abstract

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Bibliographical note

UN SDGs

Keywords

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