As Light-Emitting Diode (LED)'s increasingly displace incandescent lighting over the next few years, general
applications of Visible Light Communication (VLC) technology are expected to include wireless internet access,
vehicle-to-vehicle communications, broadcast from LED signage, and machine-to-machine communications. An
objective in this paper is to reveal the influence of system parameters on the power distribution and communication
quality, in a general plural sources VLC system. It is demonstrated that sources' Half-Power Angles (HPA), receivers'
Field-Of Views (FOV), sources layout and the power distribution among sources are significant impact factors. Based on
our findings, we developed a method to adaptively change working status of each LED respectively according to users'
locations. The program minimizes total power emitted while simultaneously ensuring sufficient light intensity and
communication quality for each user. The paper also compares Orthogonal Frequency-Division Multiplexing (OFDM)
and On-Off Keying (OOK) signals performance in indoor optical wireless communications. The simulation is carried out
for different locations where different impulse response distortions are experienced. OFDM seems a better choice than
prevalent OOK for indoor VLC due to its high resistance to multi-path effect and delay spread. However, the peak-to-average
power limitations of the method must be investigated for lighting LEDs.
A Free Space Optical Communication (FSO) system transmits modulated light
through atmospheric media. Because of the uneven distribution of refractive index result from
atmospheric turbulence, the phase distribution of light is changed leading to distortion of
wavefront and requiring reconstruction at the receiver. However, current wavefront
compensation relies on channel modeling which has difficulties in extracting channel
information from highly random turbulent atmosphere. In this paper, a wavefront
reconstruction system based on neural network algorithm is constructed. The neural network
requires little channel information but predicts distortion by past experience. Then, distorted
phase distribution is adaptively revised when light passes through a piezoelectric ceramic
deformable mirror controlled by neural network. Dynamic study factors are added to neural
network algorithm as improvement which adjusts the study speed of the system according to
turbulence intensity providing best result between respond time and reconstruction accuracy.
In addition, light transmitted in atmospheric channel is studied.
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