Earticle

초록

영어

In outdoor optical wireless communication systems, weather-induced turbulence affects optical signals, resulting in distortion, thereby, degradation in communication performance. The channel model including turbulence is used for estimating the performance of optical wireless communication system under turbulence. A deep learning algorithm is developed to classify degree of turbulence. This study is based on channel classification using a convolutional neural network for a 4-PSK optical wireless communication system. The channel characteristics are generated following the gamma-gamma distribution. By labeling each data point and distorted constellation for different degrees of turbulence, the deep learning model is trained, and its classification performance is evaluated.

목차

Abstract
I. INTRODUCTION
II. CLASSIFICATION OF TURBULENCE
A. Channel classification
B. Turbulence channel models
C. Constellations of received signals
III. RESULT AND DISCUSSION
A. Data distribution to train, validate, and test
B. Deep learaning based channel classification
IV. CONCLUSION
REFERENCES

저자

• Yongwoon Hwang [ Department of Electronic Engineering, Chosun University ]
• Chung Ghiu Lee [ Department of Electronic Engineering, Chosun University ] Corresponding Author

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