--- a/src/propagation/doc/propagation.rst	Fri Jan 22 11:18:58 2016 -0800
+++ b/src/propagation/doc/propagation.rst	Mon Jan 25 00:58:38 2016 +0100
@@ -267,21 +267,27 @@
 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::
 

--- a/src/propagation/model/propagation-loss-model.h	Fri Jan 22 11:18:58 2016 -0800
+++ b/src/propagation/model/propagation-loss-model.h	Mon Jan 25 00:58:38 2016 +0100
@@ -629,17 +629,24 @@
  * \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