TY - GEN
T1 - Intelligent Analog Radio Over Fiber aided C-RAN for Mitigating Nonlinearity and Improving Robustness
AU - Li, Yichuan
AU - El-Hajjar, Mohammed
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 A-RoF 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.
AB - 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 A-RoF 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.
KW - Analogue radio over fiber (A-RoF)
KW - Centralised radio access network (C- RAN)
KW - fronthaul
KW - logistic regression classification
KW - supervised learning
UR - https://www.scopus.com/pages/publications/85141161591
U2 - 10.1109/ISCC55528.2022.9912819
DO - 10.1109/ISCC55528.2022.9912819
M3 - 会议稿件
AN - SCOPUS:85141161591
T3 - Proceedings - IEEE Symposium on Computers and Communications
BT - 2022 IEEE Symposium on Computers and Communications, ISCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th IEEE Symposium on Computers and Communications, ISCC 2022
Y2 - 30 June 2022 through 3 July 2022
ER -