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Sparsity Signal Detection for Indoor GSSK-VLC System.

Zuo, T., Wang, F. and Zhang, J., 2021. Sparsity Signal Detection for Indoor GSSK-VLC System. IEEE Transactions on Vehicular Technology, 70 (12), 12975-12984.

Full text available as:

SSD_TVT_final_file.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/TVT.2021.3122968


In this paper, the signal detection problem in indoor visible light communication (VLC) system aided by generalized space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by exploiting the sparse reconstruction algorithms in compressed sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed based on singular value decomposition (SVD), which theoretically guarantees the feasibility of utilizing CS based sparse signal detection method in indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the GSSK signals are efficiently detected in the considered indoor GSSK-VLC system. Furthermore, a more efficient detection algorithm combined with OMP and maximum likelihood (ML) is also presented especially for SSK scenario. Finally, the effectiveness of the proposed sparsity aided detection algorithms in indoor GSSK-VLC system are verified by computer simulations. The results show that the proposed algorithms can achieve better bit error rate (BER) and lower computation complexity than ML based detection method. Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed in the SSK scenario and about 5 dB in case of a GSSK scenario upon employing our proposed detection methods.

Item Type:Article
Additional Information:This research was supported in part by the National Natural Science Foundation of China under Grant 61571401, U1736107 and 61901366, in part by the National Natural Science Foundation of Henan Province under Grant 192102210088, in part by the National Key Research and Development Program under Grant 2019QY0302, and in part bythe Innovative Talent of Colleges and University of Henan Province under Grant 18HASTIT021.
Uncontrolled Keywords:Visible light communication (VLC), generalized space shift keying (GSSK), compressed sensing (CS), maximum likelihood (ML), signal detection.
Group:Faculty of Science & Technology
ID Code:36136
Deposited By: Symplectic RT2
Deposited On:25 Oct 2021 13:04
Last Modified:14 Mar 2022 14:29


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