Ground Simulation and Pattern Recognition for Arbitrary Distance Optical Transmission of a Free-Space Laser Communication System

被引:0
|
作者
Masaea, Alex [1 ]
Kubokale, Martin [1 ]
Kumar, Amit Krishan [2 ,3 ]
Kumar, Rahul R. [1 ]
Assaf, Mansour H. [1 ]
Kumar, Sushil [1 ]
机构
[1] Univ South Pacific, Sch Informat Technol Engn Math & Phys, Suva, Fiji
[2] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[3] Duy Tan Univ, Fac Elect Elect Engn, Da Nang 550000, Vietnam
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Artificial intelligence; Optical diffraction; Pattern recognition; Laser beams; Laser modes; Atmospheric modeling; Optical transmitters; Grounding; Free-space laser communication system; artificial intelligence; entangled ladder network; ground simulation; isochronous metric; PLATFORM;
D O I
10.1109/ACCESS.2024.3453434
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Ground simulation methods have gained significant research attention due to their effectiveness. We propose a ground simulation method for space laser communication with a transmission distance of 53,000 km in free space using the laser as a means of information transmission. The simulation is verified using artificial intelligence and pattern recognition techniques, with Fresnel diffraction adopted as a mathematical model to represent the system and its architecture, facilitating the creation of Matlab/Simulink blocks for simulation. OptiSystem was used to calculate the power from the transmission distance. The transmission distance determined from the system is 53,000 km, with a receiving power of 2.72 x 10(-10) W. The simulations demonstrated that the power transmitted from an Earth station at arbitrary distances up to 53,000 km in space could be used to determine the power received by the receiving station. Prediction models were integrated into mathematical models to validate the results. The model's performance was tested with up to 20% additive noise, including signal scattering, background radiation, and interference. A ladder network with entanglement and an isochronous neighbourhood function exhibited high performance in accurately predicting the output at low computation cost compared to other models. The optimized deep Bayesian network technique, on the other hand, showed a high prediction rate and accuracy, albeit at the expense of high computational cost.
引用
收藏
页码:128662 / 128676
页数:15
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