83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
import torch.nn as nn
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from torch.distributions.normal import Normal
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import math
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class GaussianChannel(nn.Module):
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"""
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Simulated communication channel that assumes a Gaussian noise model for
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taking in account interference.
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"""
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def __init__(self):
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super().__init__()
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# Initialize channel parameters
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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 - math.exp(-loss * span_length) / 2 / alpha
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eps = 0.3 * math.log(
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1 + (6 / L_s) * (
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L_eff / math.asinh(
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(math.pi ** 2)
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/ 3
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* B_2
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* L_eff
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* (B_WDM ** 2)
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)
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)
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)
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b = P_ASE_1 / (
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2
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* (N_s ** eps)
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* B_N
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* (gam ** 2)
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* L_eff * math.asinh(
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(math.pi ** 2) / 3
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* B_2
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* L_eff
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* (B_WDM ** 2)
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)
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)
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P_ch = sys_rate * (((27 * math.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 * math.log10(OSNR)
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p_N_dB = -OSNR_dB
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p_N_watt = 10**(p_N_dB/10)
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self.noise_std = math.sqrt(p_N_watt * 5000)
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def get_noise(self, rows):
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"""
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Generate Gaussian random noise according to the channel’s parameters.
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:param rows: Number of noise vectors to generate.
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:return: Matrix of shape `rows` × 2.
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"""
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return Normal(0, self.noise_std).sample((rows, 2))
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def forward(self, x):
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return x + self.get_noise(len(x))
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