/usr/lib/python2.7/dist-packages/gnuradio/fec/polar/channel_construction_bec.py is in gnuradio 3.7.11-10.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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#
# Copyright 2015 Free Software Foundation, Inc.
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
import numpy as np
import helper_functions as hf
def bec_channel(eta):
'''
binary erasure channel (BEC)
for each y e Y
W(y|0) * W(y|1) = 0 or W(y|0) = W(y|1)
transistions are 1 -> 1 or 0 -> 0 or {0, 1} -> ? (erased symbol)
'''
# looks like BSC but should be interpreted differently.
w = np.array((1 - eta, eta, 1 - eta), dtype=float)
return w
def odd_rec(iwn):
return iwn ** 2
def even_rec(iwn):
return 2 * iwn - iwn ** 2
def calc_one_recursion(iw0):
iw1 = np.zeros(2 * len(iw0)) # double values
for i in range(len(iw0)):
# careful indices screw you because paper is '1' based :(
iw1[2 * i] = odd_rec(iw0[i])
iw1[2 * i + 1] = even_rec(iw0[i])
return iw1
def calculate_bec_channel_capacities_loop(initial_channel, block_power):
# compare [0, Arikan] eq. 6
iw = np.array([initial_channel, ], dtype=float)
for i in range(block_power):
iw = calc_one_recursion(iw)
return iw
def calc_vector_capacities_one_recursion(iw0):
degraded = odd_rec(iw0)
upgraded = even_rec(iw0)
iw1 = np.empty(2 * len(iw0), dtype=degraded.dtype)
iw1[0::2] = degraded
iw1[1::2] = upgraded
return iw1
def calculate_bec_channel_capacities_vector(initial_channel, block_power):
# compare [0, Arikan] eq. 6
# this version is ~ 180 times faster than the loop version with 2**22 synthetic channels
iw = np.array([initial_channel, ], dtype=float)
for i in range(block_power):
iw = calc_vector_capacities_one_recursion(iw)
return iw
def calculate_bec_channel_capacities(eta, block_size):
# compare [0, Arikan] eq. 6
iw = 1 - eta # holds for BEC as stated in paper
lw = hf.power_of_2_int(block_size)
return calculate_bec_channel_capacities_vector(iw, lw)
def calculate_z_parameters_one_recursion(z_params):
z_next = np.empty(2 * z_params.size, dtype=z_params.dtype)
z_sq = z_params ** 2
z_low = 2 * z_params - z_sq
z_next[0::2] = z_low
z_next[1::2] = z_sq
return z_next
def calculate_bec_channel_z_parameters(eta, block_size):
# compare [0, Arikan] eq. 38
block_power = hf.power_of_2_int(block_size)
z_params = np.array([eta, ], dtype=float)
for block_size in range(block_power):
z_params = calculate_z_parameters_one_recursion(z_params)
return z_params
def design_snr_to_bec_eta(design_snr):
# minimum design snr = -1.5917 corresponds to BER = 0.5
s = 10. ** (design_snr / 10.)
return np.exp(-s)
def bhattacharyya_bounds(design_snr, block_size):
'''
Harish Vangala, Emanuele Viterbo, Yi Hong: 'A Comparative Study of Polar Code Constructions for the AWGN Channel', 2015
In this paper it is called Bhattacharyya bounds channel construction and is abbreviated PCC-0
Best design SNR for block_size = 2048, R = 0.5, is 0dB.
Compare with Arikan: 'Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels.
Proposition 5. inequalities turn into equalities for BEC channel. Otherwise they represent an upper bound.
Also compare [0, Arikan] eq. 6 and 38
For BEC that translates to capacity(i) = 1 - bhattacharyya(i)
:return Z-parameters in natural bit-order. Choose according to desired rate.
'''
eta = design_snr_to_bec_eta(design_snr)
return calculate_bec_channel_z_parameters(eta, block_size)
def plot_channel_capacities(capacity, save_file=None):
block_size = len(capacity)
try:
import matplotlib.pyplot as plt
# FUN with matplotlib LaTeX fonts! http://matplotlib.org/users/usetex.html
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.rc('figure', autolayout=True)
plt.plot(capacity)
plt.xlim([0, block_size])
plt.ylim([-0.01, 1.01])
plt.xlabel('synthetic channel number')
plt.ylabel('channel capacity')
# plt.title('BEC channel construction')
plt.grid()
plt.gcf().set_size_inches(plt.gcf().get_size_inches() * .5)
if save_file:
plt.savefig(save_file)
plt.show()
except ImportError:
pass # only plot in case matplotlib is installed
def plot_average_channel_distance(save_file=None):
eta = 0.5 # design_snr_to_bec_eta(-1.5917)
powers = np.arange(4, 26)
try:
import matplotlib.pyplot as plt
import matplotlib
# FUN with matplotlib LaTeX fonts! http://matplotlib.org/users/usetex.html
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.rc('figure', autolayout=True)
dist = []
medians = []
initial_channel = 1 - eta
for p in powers:
bs = int(2 ** p)
capacities = calculate_bec_channel_capacities(eta, bs)
avg_capacity = np.repeat(initial_channel, len(capacities))
averages = np.abs(capacities - avg_capacity)
avg_distance = np.sum(averages) / float(len(capacities))
dist.append(avg_distance)
variance = np.std(averages)
medians.append(variance)
plt.errorbar(powers, dist, yerr=medians)
plt.grid()
plt.xlabel(r'block size $N$')
plt.ylabel(r'$\frac{1}{N} \sum_i |I(W_N^{(i)}) - 0.5|$')
axes = plt.axes()
tick_values = np.array(axes.get_xticks().tolist())
tick_labels = np.array(tick_values, dtype=int)
tick_labels = ['$2^{' + str(i) + '}$' for i in tick_labels]
plt.xticks(tick_values, tick_labels)
plt.xlim((powers[0], powers[-1]))
plt.ylim((0.2, 0.5001))
plt.gcf().set_size_inches(plt.gcf().get_size_inches() * .5)
if save_file:
plt.savefig(save_file)
plt.show()
except ImportError:
pass
def plot_capacity_histogram(design_snr, save_file=None):
eta = design_snr_to_bec_eta(design_snr)
# capacities = calculate_bec_channel_capacities(eta, block_size)
try:
import matplotlib.pyplot as plt
# FUN with matplotlib LaTeX fonts! http://matplotlib.org/users/usetex.html
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.rc('figure', autolayout=True)
block_sizes = [32, 128, 512]
for b in block_sizes:
capacities = calculate_bec_channel_capacities(eta, b)
w = 1. / float(len(capacities))
weights = [w, ] * b
plt.hist(capacities, bins=b, weights=weights, range=(0.95, 1.0))
plt.grid()
plt.xlabel('synthetic channel capacity')
plt.ylabel('normalized item count')
print(plt.gcf().get_size_inches())
plt.gcf().set_size_inches(plt.gcf().get_size_inches() * .5)
if save_file:
plt.savefig(save_file)
plt.show()
except ImportError:
pass
def main():
print 'channel construction main'
n = 11
block_size = int(2 ** n)
design_snr = -1.59
eta = design_snr_to_bec_eta(design_snr)
# print(calculate_bec_channel_z_parameters(eta, block_size))
# capacity = calculate_bec_channel_capacities(eta, block_size)
# plot_average_channel_distance()
calculate_bec_channel_z_parameters(eta, block_size)
if __name__ == '__main__':
main()
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