Cleanup channel model

This commit is contained in:
Mattéo Delabre 2019-12-15 23:12:00 -05:00
parent 3638749998
commit 722c02ef24
Signed by: matteo
GPG Key ID: AE3FBD02DC583ABB
1 changed files with 69 additions and 66 deletions

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@ -1,75 +1,78 @@
import torch.nn as nn import torch.nn as nn
import torch from torch.distributions.normal import Normal
import math import math
import numpy as np
def channel_OSNR():
Sys_rate = 32e9
r = 0.05
Dispersion = 16.48e-6
B_2 = Dispersion
Non_linear_index = 1.3e3
Gam = Non_linear_index
Loss = 10**20
Alpha = Loss
Span_count = 20
N_s = Span_count
Span_length = 10e5 # (km)
L_s = Span_length
Noise_figure = 10**0.5 # (dB)
h = 6.6261e-34
v = 299792458
B_WDM = Sys_rate*(1+r)
B_N = 0.1
P_ASE_1 = h*v*B_N*(Loss*Span_length*Noise_figure-1)
P_ASE = P_ASE_1 * Span_count
L_eff = 1-np.exp(-Loss*Span_length)/2/Alpha
eps = 0.3*np.log(1+(6/L_s)*(L_eff/np.arcsinh((np.pi**2/3)*B_2*L_eff*B_WDM**2)))
b = P_ASE_1/(2*(N_s**eps)*B_N*(Gam**2)*L_eff*np.arcsinh((np.pi**2/3)*B_2*L_eff*B_WDM**2))
P_ch = Sys_rate*(((27*np.pi*B_2/16)*b)**(1/3))
OSNR = (2*P_ch/3/P_ASE)
OSNR_dB = 10*np.log10(OSNR)
return OSNR_dB
def Const_Points_Pow(IQ):
"""
Calculate the average power of a constellation.
"""
p_enc_avg = (torch.norm(IQ, dim=1) ** 2).mean()
p_enc_avg_dB = 10 * torch.log10(p_enc_avg)
return p_enc_avg_dB
def Pow_Noise(CH_OSNR):
"""
Calculate the power of channel noise.
"""
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(variance, shape):
"""
Compute the Gaussian noise to be added to each vector to simulate passing
through a channel.
"""
return torch.distributions.normal.Normal(
0, math.sqrt(variance * 5000)
).sample(shape)
class GaussianChannel(nn.Module): class GaussianChannel(nn.Module):
def __init__(self): def __init__(self):
super().__init__() super().__init__()
# Initialize channel parameters
sys_rate = 32e9
r = 0.05
dispersion = 16.48e-6
B_2 = dispersion
non_linear_index = 1.3e3
gam = non_linear_index
loss = 10**20
alpha = loss
span_count = 20
N_s = span_count
span_length = 10e5 # (km)
L_s = span_length
noise_figure = 10 ** 0.5 # (dB)
h = 6.6261e-34
v = 299792458
B_WDM = sys_rate * (1 + r)
B_N = 0.1
P_ASE_1 = h * v * B_N * (loss * span_length * noise_figure - 1)
P_ASE = P_ASE_1 * span_count
L_eff = 1 - math.exp(-loss * span_length) / 2 / alpha
eps = 0.3 * math.log(
1 + (6 / L_s) * (
L_eff / math.asinh(
(math.pi ** 2)
/ 3
* B_2
* L_eff
* (B_WDM ** 2)
)
)
)
b = P_ASE_1 / (
2
* (N_s ** eps)
* B_N
* (gam ** 2)
* L_eff * math.asinh(
(math.pi ** 2) / 3
* B_2
* L_eff
* (B_WDM ** 2)
)
)
P_ch = sys_rate * (((27 * math.pi * B_2 / 16) * b) ** (1 / 3))
OSNR = (2 * P_ch / 3 / P_ASE)
OSNR_dB = 10 * math.log10(OSNR)
p_N_dB = -OSNR_dB
p_N_watt = 10**(p_N_dB/10)
self.noise_std = math.sqrt(p_N_watt * 5000)
def get_noise(self, rows):
"""
Generate Gaussian random noise according to the channels parameters.
:param rows: Number of noise vectors to generate.
:return: Matrix of shape `rows` × 2.
"""
return Normal(0, self.noise_std).sample((rows, 2))
def forward(self, x): def forward(self, x):
Noise_Variance = Pow_Noise(channel_OSNR()) return x + self.get_noise(len(x))
Noise_Volts = Channel_Noise_Model(Noise_Variance, [len(x), 2])
return x + Noise_Volts