Exact(7)
We also provide a good comparison of FSO communication noise models considering a moderate atmospheric turbulence condition.
Section 3 discusses the structure of the LDPC code used for the performance improvement of the FSO communication system under atmospheric turbulence condition.
We considered the atmospheric turbulence condition, background noise from the extraneous noise sources (i.e. stars and sun), and the Gaussian noise (which is considered as the SDGN and SIGN).
It is found that the spectra of fluids in the wake are sensitive to the oncoming turbulence condition for the 2-D ridge, which is not true for the 3-D hill.
For each turbulence condition, three mean bulk flow velocities of 4.0, 6.2, and 8.3 m/s, along with two fuel air equivalence ratios of 0.6 and 0.7 were tested.
We show that large gains are possible using an LDPC decoder (i.e. at a bit error rate of 10-3, there is a gain of about 6 dB considering the SDGN model in case of no turbulence condition at λ = 10 dB), when the channel state information (CSI) is known at the receiver.
Similar(53)
Specifically, the result show that, for relatively intense turbulence conditions, the spatially-averaged turbulent burning velocity is proportional to the root-mean-square of the streamwise velocity.
A freely propagating turbulent premixed stoichiometric flame is simulated for both low and high turbulence conditions i.e., the rms values of turbulent velocity fluctuations normalised by the laminar flame speed are of order 1 and 10.
The newly proposed combustion scheme can reasonably describe the interaction of chemistry and turbulent flow over a wider range of turbulence conditions.
Whereas, moving from medium turbulence conditions to weak turbulence conditions makes the RF links dominate the system performance, where the diversity order equals K 1−N 1−N=K 2−N 2+1−N
Two main cases are shown in this figure: severe atmospheric turbulence conditions (α=4.341 and β=1.309) and weak atmospheric turbulence conditions (α=6.993 and β=5.460).
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