diff --git a/constellation/GaussianChannel.py b/constellation/GaussianChannel.py
index 09638d7..4efc083 100644
--- a/constellation/GaussianChannel.py
+++ b/constellation/GaussianChannel.py
@@ -1,5 +1,6 @@
 import torch.nn as nn
 import torch
+import math
 import numpy as np
 
 
@@ -44,24 +45,24 @@ def Const_Points_Pow(IQ):
     return p_enc_avg_dB
 
 
-def Pow_Noise(CH_OSNR, CPP):
+def Pow_Noise(CH_OSNR):
     """
     Calculate the power of channel noise.
     """
-    P_N_dB = CPP - CH_OSNR
+    P_N_dB = -CH_OSNR
     p_N_watt = 10**(P_N_dB/10)
     Var_Noise = p_N_watt
     return Var_Noise
 
 
-def Channel_Noise_Model(NV, S):
+def Channel_Noise_Model(variance, shape):
     """
     Compute the Gaussian noise to be added to each vector to simulate passing
     through a channel.
     """
     return torch.distributions.normal.Normal(
-        0, torch.sqrt(NV*10000)
-    ).sample(S)
+        0, math.sqrt(variance * 5000)
+    ).sample(shape)
 
 
 class GaussianChannel(nn.Module):
@@ -69,6 +70,6 @@ class GaussianChannel(nn.Module):
         super().__init__()
 
     def forward(self, x):
-        Noise_Variance = Pow_Noise(channel_OSNR(), Const_Points_Pow(x))
+        Noise_Variance = Pow_Noise(channel_OSNR())
         Noise_Volts = Channel_Noise_Model(Noise_Variance, [len(x), 2])
         return x + Noise_Volts