In [1]:
# importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
In [2]:
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
In [3]:
# number of realizations along which to average the psd estimate
n_real = 100
# modulation scheme and constellation points
constellation = [ -1, 1 ]
# number of symbols
n_symb = 100
t_symb = 1.0
chips_per_symbol = 8
samples_per_chip = 8
samples_per_symbol = samples_per_chip * chips_per_symbol
# parameters for frequency regime
N_fft = 512
omega = np.linspace( -np.pi, np.pi, N_fft )
f_vec = omega / ( 2 * np.pi * t_symb / samples_per_symbol )
In [4]:
# define rectangular function responses
rect = np.ones( samples_per_symbol )
rect /= np.linalg.norm( rect )
# number of realizations along which to average the psd estimate
n_real = 10
# initialize two-dimensional field for collecting several realizations along which to average
RECT_PSD = np.zeros( (n_real, N_fft ) )
DSSS_PSD = np.zeros( (n_real, N_fft ) )
# get chips and signature
# NOTE: looping until number of +-1 chips in | sum ones - 0.5 N_chips | < 0.2 N_chips,
# i.e., number of +1,-1 is approximately 1/2 (up to 20 percent)
while True:
dsss_chips = (-1) ** np.random.randint( 0, 2, size = chips_per_symbol )
if np.abs( np.sum( dsss_chips > 0) - chips_per_symbol/2 ) / chips_per_symbol < .2:
break
# generate signature out of chips by putting samples_per_symbol samples with chip amplitude
# normalize signature to energy 1
dsss_signature = np.ones( samples_per_symbol )
for n in range( chips_per_symbol ):
dsss_signature[ n * samples_per_chip : (n+1) * samples_per_chip ] *= dsss_chips[ n ]
dsss_signature /= np.linalg.norm( dsss_signature )
# activate switch if chips should be resampled for every simulation
# this would average (e.g., for PSD) instead of showing "one reality"
new_chips_per_sim = 1
# loop for realizations
for k in np.arange( n_real ):
if new_chips_per_sim:
# resample signature using identical method as above
while True:
dsss_chips = (-1) ** np.random.randint( 0, 2, size = chips_per_symbol )
if np.abs( np.sum( dsss_chips > 0) - chips_per_symbol/2 ) / chips_per_symbol < .2:
break
# get signature
dsss_signature = np.ones( samples_per_symbol )
for n in range( chips_per_symbol ):
dsss_signature[ n * samples_per_chip : (n+1) * samples_per_chip ] *= dsss_chips[ n ]
dsss_signature /= np.linalg.norm( dsss_signature )
# generate random binary vector and modulate
data = np.random.randint( 2, size = n_symb )
mod = [ constellation[ d ] for d in data ]
# get signals by putting symbols and filtering
s_up = np.zeros( n_symb * samples_per_symbol )
s_up[ :: samples_per_symbol ] = mod
# apply RECTANGULAR and CDMA shaping in time domain
s_rect = np.convolve( rect, s_up )
s_dsss = np.convolve( dsss_signature, s_up )
# get spectrum
RECT_PSD[ k, :] = np.abs( np.fft.fftshift( np.fft.fft( s_rect, N_fft ) ) )**2
DSSS_PSD[ k, :] = np.abs( np.fft.fftshift( np.fft.fft( s_dsss, N_fft ) ) )**2
# average along realizations
RECT_av = np.average( RECT_PSD, axis=0 )
RECT_av /= np.max( RECT_av )
DSSS_av = np.average( DSSS_PSD, axis=0 )
DSSS_av /= np.max( DSSS_av )
In [5]:
# show limited amount of symbols in time domain
N_syms_plot = 5
t_plot = np.arange( 0, N_syms_plot * t_symb, t_symb / samples_per_symbol )
# plot
plt.figure()
plt.subplot(121)
plt.plot( t_plot, s_rect[ : N_syms_plot * samples_per_symbol], linewidth=2.0, label='Rect')
plt.plot( t_plot, s_dsss[ : N_syms_plot * samples_per_symbol ], linewidth=2.0, label='DS-SS')
plt.ylim( (-1.1, 1.1 ) )
plt.grid( True )
plt.legend(loc='upper right')
plt.xlabel('$t/T$')
plt.title('$s(t)$')
plt.subplot(122)
np.seterr(divide='ignore') # ignore warning for logarithm of 0
plt.plot( f_vec, 10*np.log10( RECT_av ), linewidth=2.0, label='Rect., sim.' )
plt.plot( f_vec, 10*np.log10( DSSS_av ), linewidth=2.0, label='DS-SS, sim.' )
np.seterr(divide='warn') # enable warning for logarithm of 0
plt.grid(True)
plt.legend(loc='lower right')
plt.ylim( (-60, 10 ) )
plt.xlabel('$fT$')
plt.title('$|S(f)|^2$')
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