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Demodulator.py
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from typing import Literal
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sci
class Demodulator:
def __init__(self, bps=1e6, fs=1e8, filter=[], padding=0):
self.setBps(bps).setFs(fs).setFilter(filter).setPadding(padding)
# Set bps
def setBps(self, bps):
self.bps = bps
return self
# Set fs
def setFs(self, fs):
self.fs = fs
return self
# Set filter
def setFilter(self, filter):
try:
self.Ns = int(self.fs / self.bps)
self.filter = sci.resample(filter, self.Ns)
except AttributeError:
print("Please set bps and fs first.")
return self
# Set padding
def setPadding(self, padding):
self.padding = padding
return self
def setSignal(self, signal):
self.signal = signal
return self
def setCorrectBits(self, correctBits, method: Literal['OOK', 'QPSK', '16QAM', '32QAM', '64QAM']):
self.correct = correctBits
self.method = method
return self
# Demodulate - signal coherence
def demodulate(self):
sig = np.fft.fftshift(np.fft.ifft(np.fft.fftshift(self.filter)))
self.N = self.signal.size
self.Ns = int(self.fs / self.bps)
self.n = self.N // self.Ns
self.samples = np.zeros(shape=self.correct.shape, dtype=complex)
self.bits = np.zeros(shape=self.correct.shape, dtype=int)
for i in range(self.n-self.padding*2):
self.samples[i] = np.sum(self.signal[((i+self.padding)*self.Ns):(i+self.padding+1)*self.Ns] * sig)
self.bits[i] = np.argmin(np.abs(symbols[self.method] - self.samples[i]))
return self
def plotConstellation(self, legend:bool=True):
if not hasattr(self, 'samples'):
self.demodulate()
plt.figure(num='Constellation')
plt.subplot().set_aspect('equal')
plt.axvline(0, c='g')
plt.axhline(0, c='g')
for i in range(symbols[self.method].size):
mask = self.correct == i
plt.scatter(np.real(self.samples[mask]), np.imag(self.samples[mask]), marker='o', label=str(i), s=25)
if legend:
plt.legend()
plt.xlabel('Real')
plt.ylabel('Imag')
plt.title('Constellation')
plt.grid(True, 'major')
m = np.max(np.abs(np.hstack((np.real(self.samples), np.imag(self.samples))))) * 1.5
plt.axis(np.array([-1, 1, -1, 1]) * m)
plt.show()
class WDMDemodulator(Demodulator):
def __init__(self, bps=1e6, fs=1e8, filter=[], padding=0):
super().__init__(bps, fs, filter, padding)
def setWDM(self, freqCenter=0.0, freqInterval=5e10, channels=1):
self.freqCenter = freqCenter
self.freqInterval = freqInterval
self.channels = channels
self.freqs = np.linspace(0, self.freqInterval*(self.channels-1), self.channels) - self.freqInterval*(self.channels-1)/2 + self.freqCenter
return self
def demodulate(self):
sup = Demodulator(bps=self.bps, fs=self.fs, filter=self.filter, padding=self.padding)
self.n = self.correct.shape[-1] + 2*self.padding
self.Ns = int(self.fs / self.bps)
self.N = self.Ns * self.n
self.t = np.arange(0, self.N/self.fs, 1/self.fs)[:self.N]
self.f = np.arange(-self.fs/2, self.fs/2, self.bps/self.n)[:self.N]
def ExtractFreq(freqCenter):
extracted = np.fft.fftshift(np.fft.fft(self.signal * np.exp(-1j * 2 * np.pi * freqCenter * self.t)))
extracted[np.abs(self.f)>self.freqInterval/2] = 0
return np.fft.ifft(np.fft.fftshift(extracted))
self.samples = np.zeros(shape=self.correct.shape, dtype=complex)
self.bits = np.zeros(shape=self.correct.shape, dtype=int)
for i in range(self.channels):
sup.setCorrectBits(self.correct[i, :], self.method).setSignal(ExtractFreq(self.freqs[i])).demodulate()
self.samples[i, :] = sup.samples
self.bits[i, :] = sup.bits
return self
def plotConstellation(self, legend = True,
codition=lambda x: np.ones(x.shape, dtype=bool)):
globalMask = codition(self.correct)
plt.figure(num='Constellation')
plt.subplot().set_aspect('equal')
plt.axvline(0, c='g')
plt.axhline(0, c='g')
for i in range(symbols[self.method].size):
mask = (self.correct == i) & globalMask
plt.scatter(np.real(self.samples[mask]), np.imag(self.samples[mask]), marker='o', label=str(i), s=25)
if legend:
plt.legend()
plt.xlabel('Real')
plt.ylabel('Imag')
plt.title('Constellation')
plt.grid(True, 'major')
m = np.max(np.abs(np.hstack((np.real(self.samples), np.imag(self.samples))))) * 1.5
plt.axis(np.array([-1, 1, -1, 1]) * m)
plt.show()
# Symbol mapping
# By default, the least energy of all symbols is sqrt(2).
OOK = np.array([0, 1], dtype=complex)
_temp = np.array([-1, 1], dtype=complex)
QPSK = (_temp + 1j * _temp[:, None]).flatten()
_temp = np.array([-3, -1, 1, 3], dtype=complex)
QAM16 = (_temp + 1j * _temp[:, None]).flatten()
_temp = np.array([-5, -3, -1, 1, 3, 5], dtype=complex)
QAM32 = (_temp + 1j * _temp[:, None]).flatten()
QAM32 = QAM32[(np.abs(QAM32) < 5*1.414)]
_temp = np.array([-7, -5, -3, -1, 1, 3, 5, 7], dtype=complex)
QAM64 = (_temp + 1j * _temp[:, None]).flatten()
symbols = {'OOK': OOK, 'QPSK': QPSK, '16QAM': QAM16, '32QAM': QAM32, '64QAM': QAM64}
del OOK, _temp, QPSK, QAM16, QAM32, QAM64
def symbolMapping(bits: list | np.ndarray,
method: Literal['OOK', 'QPSK', '16QAM', '32QAM', '64QAM', 'PAM']='OOK',
maxEnergy: float | np.number = None,
minEnergy: float | np.number = None,
averageEnergy: float | np.number = None,
symbolCount: int | np.number = None):
if method == 'PAM':
symbols['PAM'] = np.linspace(minEnergy, maxEnergy, symbolCount)
alphabet = symbols[method]
if maxEnergy is not None:
factor = np.sqrt(maxEnergy) / np.max(np.abs(alphabet))
elif minEnergy is not None:
if minEnergy != 0:
factor = np.sqrt(minEnergy) / np.min(np.abs(alphabet))
else:
print('[ERROR] Invalid minEnergy = 0.')
elif averageEnergy is not None:
factor = np.sqrt(averageEnergy) / np.mean(np.abs(alphabet))
else:
factor = 1.0
return np.array(alphabet[bits] * factor)