'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
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