Intelligent analog radio over fiber aided C-RAN for mitigating nonlinearity and improving robustness
Intelligent analog radio over fiber aided C-RAN for mitigating nonlinearity and improving robustness
As a low-cost solution for the 5G communication system, centralised radio access network (C-RAN) has been implemented in the ultra-dense environment, where radio over fiber (RoF) technology can enable reduced operational cost as well as coordinated multi-point (CoMP) despite its less robustness and reduced system performance. On the other hand, machine learning has been recognised as an efficient method for accelerating the fiber-optic communications with the aid of the advancements of the learning algorithms as well as the available high processing capabilities. In this paper, we propose a supervised learning-aided A-RoF system, where the logistic regression classification is invoked for removing the ARoF module’s need for re-customization and for boosting its performance. As a result, we can adaptively select the modulation format according to the optical power and the RF voltage, where we obtain an enhanced spectral efficiency and dynamic range (DR) by a factor of 4/3 and 19/13, respectively, while the learning network can be updated online.
Li, Yichuan
66d2dfef-f67a-4d18-9338-301ffc972595
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Li, Yichuan
66d2dfef-f67a-4d18-9338-301ffc972595
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Li, Yichuan and El-Hajjar, Mohammed
(2022)
Intelligent analog radio over fiber aided C-RAN for mitigating nonlinearity and improving robustness.
IEEE Symposium on Computers and Communications.
30 Jun - 03 Jul 2022.
6 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
As a low-cost solution for the 5G communication system, centralised radio access network (C-RAN) has been implemented in the ultra-dense environment, where radio over fiber (RoF) technology can enable reduced operational cost as well as coordinated multi-point (CoMP) despite its less robustness and reduced system performance. On the other hand, machine learning has been recognised as an efficient method for accelerating the fiber-optic communications with the aid of the advancements of the learning algorithms as well as the available high processing capabilities. In this paper, we propose a supervised learning-aided A-RoF system, where the logistic regression classification is invoked for removing the ARoF module’s need for re-customization and for boosting its performance. As a result, we can adaptively select the modulation format according to the optical power and the RF voltage, where we obtain an enhanced spectral efficiency and dynamic range (DR) by a factor of 4/3 and 19/13, respectively, while the learning network can be updated online.
Text
Intelligent Analog Radio Over Fiber aided C-RAN for Mitigating Nonlinearity and Improving Robustness
- Accepted Manuscript
More information
Accepted/In Press date: 25 April 2022
Venue - Dates:
IEEE Symposium on Computers and Communications, 2022-06-30 - 2022-07-03
Identifiers
Local EPrints ID: 457052
URI: http://eprints.soton.ac.uk/id/eprint/457052
PURE UUID: 2346959f-baa2-40f0-902d-19c06970449c
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Date deposited: 20 May 2022 16:48
Last modified: 17 Mar 2024 03:28
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Contributors
Author:
Yichuan Li
Author:
Mohammed El-Hajjar
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