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논문 기본 정보

자료유형
학술저널
저자정보
Mahesh Kumar Jha (CMR University) Rubini P (CMR University) Navin Kumar (Amrita University)
저널정보
Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.17 No.2
발행연도
2023.6
수록면
71 - 79 (9page)
DOI
10.5626/JCSE.2023.17.2.71

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초록· 키워드

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Flip orthogonal frequency division multiplexing (OFDM) is a variation of OFDM which modifies the bipolar OFDM signal in the unipolar signal by flipping the negative sign of the subcarriers. Flip-OFDM in multiple input multiple output (MIMO) visible light communication (VLC) system improves the orthogonally of the subcarriers and reduces bit error rate, which results in a higher data rate and a more robust communication system. Machine learning (ML) and deep learning (DL) techniques are being used to improve various aspects of VLC systems such as modulation, channel estimation, and MIMO design, which can result in more robust and efficient communication systems. In this paper, deep neural network (DNN), convolution neural network (CNN) and long short-term memory (LSTM) algorithms are used to analyze flip-OFDM optical MIMO VLC system. The MIMO techniques, repetitive coding (RC), spatial modulation (SM), generalized spatial modulation (generalized-SM) and spatial multiplexing (SMP) are analyzed with and without flip-OFDM. Simulation results showed that generalized-SM outperformed SM, SMP and RC with and without flip-OFDM. In both scenarios, CNN improved performance and outperformed LSTM and DNN.

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Abstract
I. INTRODUCTION
II. FLIP-OFDM BASED VLC SYSTEM
III. ML/DL APPROACH FOR FLIP-OFDM BASED VLC SYSTEM
IV. SIMULATION RESULTS AND FINDINGS
V. CONCLUSION
REFERENCES

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