Using autoencoders to optimize two-dimensional signal constellations for fiber optic communication systems
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import torch.nn as nn
from torch.distributions.normal import Normal
import math
class GaussianChannel(nn.Module):
"""
Simulated communication channel that assumes a Gaussian noise model for
taking in account interference.
"""
def __init__(self):
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 channel’s 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):
return x + self.get_noise(len(x))