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Bug 2274 - Nakagami Propagation Loss Model Doesn't Work Properly

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This commit is contained in:
Nicola Baldo
2016-01-25 00:58:38 +01:00
parent a26ecf4b24
commit 0289815ebb
2 changed files with 20 additions and 7 deletions
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14
src/propagation/doc/propagation.rst
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@@ -267,21 +267,27 @@ propagation loss.
NakagamiPropagationLossModel
============================
This propagation loss model implements Nakagami-m fast fading propagation loss model.
This propagation loss model implements the Nakagami-m fast fading
model, which accounts for the variations in signal strength due to multipath
fading. The model does not account for the path loss due to the
distance traveled by the signal, hence for typical simulation usage it
is recommended to consider using it in combination with other models
that take into account this aspect.
The Nakagami-m distribution is applied to the power level. The probability density function is defined as
.. math::
p(x; m, \omega) = \frac{2 m^m}{\Gamma(m) \omega^m} x^{2m - 1} e^{-\frac{m}{\omega} x^2} = 2 x \cdot p_{\text{Gamma}}(x^2, m, \frac{m}{\omega})
p(x; m, \omega) = \frac{2 m^m}{\Gamma(m) \omega^m} x^{2m - 1} e^{-\frac{m}{\omega} x^2} )
with :math:`m` the fading depth parameter and :math:`\omega` the average received power.
It is implemented by either a :cpp:class:`GammaRandomVariable` or a :cpp:class:`ErlangRandomVariable`
random variable.
Like in :cpp:class:ThreeLogDistancePropagationLossModel`, the :math:`m` parameter is varied
over three distance fields:
The implementation of the model allows to specify different values of
the :math:`m` parameter (and hence different fast fading profiles)
for three different distance ranges:
.. math::

13
src/propagation/model/propagation-loss-model.h
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@@ -629,17 +629,24 @@ private:
* \ingroup propagation
*
* \brief Nakagami-m fast fading propagation loss model.
*
* This propagation loss model implements the Nakagami-m fast fading
* model, which accounts for the variations in signal strength due to multipath
* fading. The model does not account for the path loss due to the
* distance traveled by the signal, hence for typical simulation usage it
* is recommended to consider using it in combination with other models
* that take into account this aspect.
*
* The Nakagami-m distribution is applied to the power level. The probability
* density function is defined as
* \f[ p(x; m, \omega) = \frac{2 m^m}{\Gamma(m) \omega^m} x^{2m - 1} e^{-\frac{m}{\omega} x^2} = 2 x \cdot p_{\text{Gamma}}(x^2, m, \frac{m}{\omega}) \f]
* \f[ p(x; m, \omega) = \frac{2 m^m}{\Gamma(m) \omega^m} x^{2m - 1} e^{-\frac{m}{\omega} x^2} \f]
* with \f$ m \f$ the fading depth parameter and \f$ \omega \f$ the average received power.
*
* It is implemented by either a ns3::GammaRandomVariable or a
* ns3::ErlangRandomVariable random variable.
*
* Like in ns3::ThreeLogDistancePropagationLossModel, the m parameter is varied
* over three distance fields:
* The implementation of the model allows to specify different values of the m parameter (and hence different fading profiles)
* for three different distance ranges:
* \f[ \underbrace{0 \cdots\cdots}_{m_0} \underbrace{d_1 \cdots\cdots}_{m_1} \underbrace{d_2 \cdots\cdots}_{m_2} \infty \f]
*
* For m = 1 the Nakagami-m distribution equals the Rayleigh distribution. Thus