Satellite Communication and Free Space Optics for Open Radio Access Network

被引:3
|
作者
Yao, Cheng-Kai [1 ]
Lin, Hsin-Piao [2 ]
Cheng, Chiun-Lang [3 ]
Li, You-Lin [1 ]
Du, Li-Yuan [1 ]
Peng, Peng-Chun [1 ]
机构
[1] Natl Taipei Univ Technol, Dept Electroopt Engn, Taipei 10608, Taiwan
[2] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 10608, Taiwan
[3] Taiwan Space Agcy, Div Ground Commun, Hsinchu 30078, Taiwan
关键词
5G mobile communication; Satellite communication; Optical fibers; Optical fiber sensors; Optical network units; Optical switches; Computer architecture; Free space optics; optical wireless communication; peak wavelength detection; satellite communication; INTELLIGENCE; HYBRID; LINKS; RAN;
D O I
10.1109/JLT.2024.3362696
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper verifies the feasibility and effectiveness of utilizing an integration technology of free-space optics (FSO) and satellite communications for an open radio access network (O-RAN) system while simultaneously transmitting fiber-optic sensing signals over the same FSO link. The achievement of O-RAN is to improve the flexibility, openness, and cost-effectiveness of current and next-generation wireless networks. Compared with radio wave-based communication, optical wireless communication (OWC), such as FSO, has many advantages, such as high-speed transmission, data confidentiality, and freedom from electromagnetic interference. Therefore, the use of FSO as a transmission link between ground radar transceivers for satellite communication and O-RAN systems will enhance the communication quality of satellite communication-based O-RAN systems. In particular, FSOs are small and easy to place at ground radar transceiver stations, satellite trucks, and communication target points. This makes it easier to quickly switch the transmission route of the radar transceiver station to different communication transmission and sensing monitoring target scenarios than physical optical fiber cable. In addition, in the fiber optic sensing part, use a machine learning model that combines advanced conditional generative adversarial network (CGAN) and convolutional neural network (CNN) models. This model aims to generate a large amount of sensor data and improve the sensing performance of fiber sensing systems. The experimental findings unambiguously show that the proposed integrated system achieves normal functioning in the communication part and outstanding temperature sensing measurement accuracy in the sensing part.
引用
收藏
页码:3546 / 3553
页数:8
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