def defaultParameters(D=16.4640, Fc=1.9341e+14):
# D = 16.4640 # makes beta2 = 21
lambda_ = c / Fc
beta2 = D * 1e-6 * lambda_**2 / (2 * np.pi * c) * 1e27
param = cu.AttrDict()
param.lambda_ = lambda_ # Wavelength
param.Fc = Fc # Carrier frequncy
param.PolMux = 1 # 0 when single polarization, 1 with polarization multiplexing
param.gamma = 1.3 # Nonlinearity coefficient in [1/W/km]
param.D = D
param.beta2 = beta2 # Dispersion coefficient [ps^2/km]
param.alpha = 0.2 # Fiber loss coefficient [dB/km]
param.Nspan = 20 # Number of spans
param.L = 100 # Span length [km]
param.PD = 0 # Pre-dispersion [ps^2]
param.PdBm = 2 # Average input power [dBm]
param.BaudRate = 32 # Baud-rate [GHz]
param.ChSpacing = 50 # Channel spacing [GHz]
param.kur = 1.32 # Second order modulation factor <|a|^4>/<|a|^2>^2
param.kur3 = 1.96 # Third order modulation factor <|a|^6>/<|a|^2>^3
param.N_mc = int(1e6) # Number of integration points in algorithm
param.NF = 5 # EDFA Noise figure
return param
def defaultParameters(D=16.4640, Fc=1.9341e+14):
lambda_ = c / Fc
beta2 = D * 1e-6 * lambda_**2 / (2 * np.pi * c)
param = cu.AttrDict()
param.nPol = 2
param.lambda_ = lambda_
param.Fc = Fc
param.D = D
param.alpha = 0.2
param.beta2 = beta2
param.gamma = 1.3
param.nSpans = 10
param.spanLength = 100
param.noiseFigure = 5
param.PdBm = 1
param.Rs = 32 # e9
param.channels = np.array([-100., -50., 0., 50., 100.])
param.nChannels = len(param.channels)
param.chSpacing = 50 # usually overwritten
param.kur = 1.32
param.kur3 = 1.96
param.N_mc = int(1e6)
param.PD = 0
return param
def defaultParameters(D=16.4640, Fc=1.9341e+14, precision='double'):
lambda_ = c / Fc
beta2 = D * 1e-6 * lambda_**2 / (2 * np.pi * c)
param = cu.AttrDict()
param.M = 4
param.nPol = 2
param.sps = 16
param.nSamples = 1024
param.rollOff = 0.05
param.filterSpan = 128
param.optimizeP = False
param.PdBm = 1
param.Rs = 32e9
param.channels = np.array([-100., -50., 0., 50., 100.])
param.nChannels = len(param.channels)
param.frequencyShift = True
param.dispersionCompensation = False
param.beta2 = beta2 # with D = 16.464
param.dz = 1000000.0 # 10 * 100 * 1e3
param.Fs = 5.1200e+11
param.N = param.sps * param.nSamples
if precision == 'single':
param.realType = tf.float32
param.complexType = tf.complex64
else:
param.realType = tf.float64
param.complexType = tf.complex128
return param
def tfConstants(dtype=tf.float64):
c = cu.AttrDict()
c.two = tf.constant(2, dtype)
c.three = tf.constant(3, dtype)
c.four = tf.constant(4, dtype)
c.nine = tf.constant(9, dtype)
c.twelve = tf.constant(12, dtype)
c.sixteen = tf.constant(16, dtype)
c.eightyone = tf.constant(81, dtype)
return c
def defaultParameters(D=16.4640, Fc=1.9341e+14, precision='double'):
lambda_ = c / Fc
beta2 = D * 1e-6 * lambda_**2 / (2 * np.pi * c)
param = cu.AttrDict()
param.Fs = 5.1200e+11
param.N = 16 * 1024
param.nSteps = 1
param.stepSize = 100
param.ampScheme = 'EDFA'
param.noiseEnabled = True
param.manakovEnabled = True
param.dispersionCompensationEnabled = False # inline dispersion compensation
param.checkpointInverval = 2
param.nPol = 2
param.lambda_ = lambda_ # Wavelength
param.Fc = Fc
param.D = D
param.alpha = 0.2
param.beta2 = beta2
param.gamma = 1.3
param.nSpans = 10
param.spanLength = 100
param.noiseFigure = 5
param.intType = tf.int32
if precision == 'single':
param.realType = tf.float32
param.complexType = tf.complex64
else:
param.realType = tf.float64
param.complexType = tf.complex128
param.stepSizeTemplate = logStepSizes(param.spanLength, param.alpha,
param.nSteps)
return param
def defaultParameters(D=16.4640, Fc=1.9341e+14, precision='double'):
param = cu.AttrDict()
param.nChannels = 5
param.nSpans = 10
param.nPol = 2
param.sps = 16
param.nSamples = 2**10
param.Rs = 32e9
param.noiseEnabled = True
param.noiseFigure = 5
param.chSpacing = 50e9 # GHz
param.spanLength = 100 # km
param.alpha = 0.2 # db/km
param.gamma = 1.3 # W^-1/km
param.D = D # ps/(km nm)
param.simplified = 1
param.Fc = Fc
param.precision = precision
return param