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The optimal variable node degree distributions are found through density evolution using the Gaussian approximation [1].
Good variable node degrees for each class are found through density evolution using the Gaussian approximation.
The optimal degree distribution of irregular codes can be found by density evolution (DE) using the Gaussian approximation [1].
The effect of pilot-aided channel estimation is studied for Rician fading channels using the Gaussian approximation.
The optimization algorithm for the proposed codes is based on density evolution using the Gaussian approximation and optimal variable node degree distributions are found through iterative linear programming.
Figure 4 illustrates the gap between the estimation of α and β using the Gaussian approximation and the asymptotic expression or the Monte Carlo simulations.
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Second, recall that to compute EXIT curves inevitably uses the Gaussian approximation.
Due to the mathematically intractable expression of the Rician distribution, we use the Gaussian approximation to derive the error probability of coded FHSS over Rician fading channel.
A more straightforward method is to use the Gaussian approximation that depends only on integrals involving up to two eigenvalue densities.
To check the (b) claim of the theorem, we use the Gaussian approximations (20).
In [20], the MAI is analyzed using the Gaussian distribution approximation and in [21] the performance was analyzed under a fading channel scenario in the MIMO case, where in contrast to existing works, an exact characterization of the MAI in a Rayleigh fading environment is developed for MIMO-CDMA systems.
More suggestions(15)
using the Laplace approximation
using the Euler approximation
using the Thiran approximation
using the gaussian model
using the Flory approximation
using the gaussian basis
using the Korovkin approximation
using the Taylor approximation
using the gaussian probability
using the gaussian mixture
using the gaussian filter
using the Derjaguin approximation
using the gaussian dispersion
using the Padé approximation
using the gaussian process
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