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22대 대한민국 국회 국회정보길라잡이 국회의원검색
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소장정보 검색

국회도서관 홈으로 정보검색 소장정보 검색
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Title Page

Contents

Abstract 7

I. Introduction 9

II. Data Generation 14

2.1. Transmitter 15

2.2. Receiver 16

III. Proposed CNN-based OCC Receiver 18

IV. Simulation Results 23

V. Conclusion 30

References 31

List of Tables

Table 1. Impact of batch size 27

List of Figures

Fig. 1. Experimental environment. 14

Fig. 2. Transmit LED matrix and OOK modulation strategy. 15

Fig. 3. Sample images of training dataset. 17

Fig. 4. Proposed CNN structure. 20

Fig. 5. Convergence behavior for various learning rate values. 24

Fig. 6. Convergence behavior for various kernel configurations. 25

Fig. 7. Convergence behavior for various layer setups. 26

Fig. 8. Test BER performance with respect to distance. 29

초록보기

Optical wireless communication (OWC) has been considered as a complementary or alternative technologies to radio frequency (RF) communications. OWC employs visible light emitted by light-emitting-diode (LED) or laser-diode (LD) to carry information which is known as visible light communication (VLC). Optical camera communication (OCC), a subsystem of OWC, uses LEDs as the transmitter and a camera or image sensor as a receiver. OCC can provide high signal-to-noise ratio (SNR) and noninterference communication even in outdoor environments.

This thesis investigates a deep learning (DL) framework for designing OCC systems where a receiver is realized with optical cameras capturing images of transmit LEDs. The optimum decoding strategy is formulated as the maximum a posterior (MAP) estimation with a given received image. Due to the absence of analytical OCC channel models, it is challenging to derive the closed-form MAP detector. To address this issue, we employ a convolutional neural network (CNN) model at the OCC receiver. The proposed CNN approximates the optimum MAP detector that determines the most probable data symbols by observing an image of the OCC transmitter implemented by dot LED matrices. The supervised learning philosophy is adopted to train the CNN with labeled images. We collect training samples in real-measurement scenarios including heterogeneous background noise and distance setups. As a consequent, the proposed CNN-based OCC receiver can be applied to arbitrary OCC scenarios without any channel state information. The effectiveness of our model is examined in the real-world OCC setup with Raspberry Pi cameras. The experimental results demonstrate that the proposed CNN architecture performs better than other DL models.

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