76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
import torch.nn as nn
|
|
import torch
|
|
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):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x):
|
|
Noise_Variance = Pow_Noise(channel_OSNR())
|
|
Noise_Volts = Channel_Noise_Model(Noise_Variance, [len(x), 2])
|
|
return x + Noise_Volts
|