75 lines
1.8 KiB
Python
75 lines
1.8 KiB
Python
import torch.nn as nn
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import torch
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import numpy as np
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def channel_OSNR():
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Sys_rate = 32e9
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r = 0.05
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Dispersion = 16.48e-6
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B_2 = Dispersion
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Non_linear_index = 1.3e3
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Gam = Non_linear_index
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Loss = 10**20
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Alpha = Loss
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Span_count = 20
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N_s = Span_count
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Span_length = 10e5 # (km)
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L_s = Span_length
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Noise_figure = 10**0.5 # (dB)
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h = 6.6261e-34
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v = 299792458
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B_WDM = Sys_rate*(1+r)
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B_N = 0.1
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P_ASE_1 = h*v*B_N*(Loss*Span_length*Noise_figure-1)
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P_ASE = P_ASE_1 * Span_count
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L_eff = 1-np.exp(-Loss*Span_length)/2/Alpha
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eps = 0.3*np.log(1+(6/L_s)*(L_eff/np.arcsinh((np.pi**2/3)*B_2*L_eff*B_WDM**2)))
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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))
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P_ch = Sys_rate*(((27*np.pi*B_2/16)*b)**(1/3))
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OSNR = (2*P_ch/3/P_ASE)
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OSNR_dB = 10*np.log10(OSNR)
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return OSNR_dB
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def Const_Points_Pow(IQ):
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"""
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Calculate the average power of a set of vectors.
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"""
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p_enc_avg = (torch.norm(IQ, dim=1) ** 2).mean()
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p_enc_avg_dB = 10 * torch.log10(p_enc_avg)
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return p_enc_avg_dB
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def Pow_Noise(CH_OSNR, CPP):
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"""
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Calculate the power of channel noise.
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"""
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P_N_dB = CPP - CH_OSNR
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p_N_watt = 10**(P_N_dB/10)
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Var_Noise = p_N_watt
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return Var_Noise
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def Channel_Noise_Model(NV, S):
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"""
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Compute the Gaussian noise to be added to each vector to simulate passing
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through a channel.
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"""
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return torch.distributions.normal.Normal(
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0, torch.sqrt(NV*5000)
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).sample(S)
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class GaussianChannel(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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Noise_Variance = Pow_Noise(channel_OSNR(), Const_Points_Pow(x))
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Noise_Volts = Channel_Noise_Model(Noise_Variance, [len(x), 2])
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return x + Noise_Volts
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