gnuradio / grdigital / examples / berawgn.py @ master
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#!/usr/bin/env python


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#

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# Copyright 2012,2013 Free Software Foundation, Inc.

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#

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# This file is part of GNU Radio

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#

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# GNU Radio is free software; you can redistribute it and/or modify

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# it under the terms of the GNU General Public License as published by

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# the Free Software Foundation; either version 3, or (at your option)

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# any later version.

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#

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# GNU Radio is distributed in the hope that it will be useful,

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# but WITHOUT ANY WARRANTY; without even the implied warranty of

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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

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# GNU General Public License for more details.

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#

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# You should have received a copy of the GNU General Public License

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# along with GNU Radio; see the file COPYING. If not, write to

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# the Free Software Foundation, Inc., 51 Franklin Street,

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# Boston, MA 021101301, USA.

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#

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"""

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BER simulation for QPSK signals, compare to theoretical values.

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Change the N_BITS value to simulate more bits per Eb/N0 value,

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thus allowing to check for lower BER values.

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Lower values will work faster, higher values will use a lot of RAM.

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Also, this app isn't highly optimizedthe flow graph is completely

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reinstantiated for every Eb/N0 value.

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Of course, expect the maximum value for BER to be one order of

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magnitude below what you chose for N_BITS.

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"""

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import math 
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import numpy 
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from gnuradio import gr, digital 
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from gnuradio import analog 
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from gnuradio import blocks 
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import sys 
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try:

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from scipy.special import erfc 
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except ImportError: 
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print "Error: could not import scipy (http://www.scipy.org/)" 
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sys.exit(1)

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try:

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import pylab 
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except ImportError: 
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print "Error: could not import pylab (http://matplotlib.sourceforge.net/)" 
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sys.exit(1)

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# Best to choose powers of 10

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N_BITS = 1e7

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RAND_SEED = 42

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def berawgn(EbN0): 
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""" Calculates theoretical bit error rate in AWGN (for BPSK and given Eb/N0) """

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return 0.5 * erfc(math.sqrt(10**(float(EbN0)/10))) 
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class BitErrors(gr.hier_block2): 
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""" Two inputs: true and received bits. We compare them and

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add up the number of incorrect bits. Because integrate_ff()

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can only add up a certain number of values, the output is

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not a scalar, but a sequence of values, the sum of which is

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the BER. """

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def __init__(self, bits_per_byte): 
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gr.hier_block2.__init__(self, "BitErrors", 
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gr.io_signature(2, 2, gr.sizeof_char), 
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gr.io_signature(1, 1, gr.sizeof_int)) 
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# Bit comparison

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comp = blocks.xor_bb() 
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intdump_decim = 100000

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if N_BITS < intdump_decim:

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intdump_decim = int(N_BITS)

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self.connect(self, 
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comp, 
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blocks.unpack_k_bits_bb(bits_per_byte), 
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blocks.uchar_to_float(), 
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blocks.integrate_ff(intdump_decim), 
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blocks.multiply_const_ff(1.0/N_BITS),

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self)

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self.connect((self, 1), (comp, 1)) 
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class BERAWGNSimu(gr.top_block): 
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" This contains the simulation flow graph "

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def __init__(self, EbN0): 
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gr.top_block.__init__(self)

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self.const = digital.qpsk_constellation()

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# Source is N_BITS bits, nonrepeated

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data = map(int, numpy.random.randint(0, self.const.arity(), N_BITS/self.const.bits_per_symbol())) 
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src = blocks.vector_source_b(data, False)

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mod = digital.chunks_to_symbols_bc((self.const.points()), 1) 
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add = blocks.add_vcc() 
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noise = analog.noise_source_c(analog.GR_GAUSSIAN, 
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self.EbN0_to_noise_voltage(EbN0),

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RAND_SEED) 
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demod = digital.constellation_decoder_cb(self.const.base())

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ber = BitErrors(self.const.bits_per_symbol())

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self.sink = blocks.vector_sink_f()

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self.connect(src, mod, add, demod, ber, self.sink) 
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self.connect(noise, (add, 1)) 
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self.connect(src, (ber, 1)) 
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def EbN0_to_noise_voltage(self, EbN0): 
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""" Converts Eb/N0 to a complex noise voltage (assuming unit symbol power) """

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return 1.0 / math.sqrt(self.const.bits_per_symbol() * 10**(float(EbN0)/10)) 
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def simulate_ber(EbN0): 
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""" All the work's done here: create flow graph, run, read out BER """

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print "Eb/N0 = %d dB" % EbN0 
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fg = BERAWGNSimu(EbN0) 
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fg.run() 
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return numpy.sum(fg.sink.data())

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if __name__ == "__main__": 
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EbN0_min = 0

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EbN0_max = 15

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EbN0_range = range(EbN0_min, EbN0_max+1) 
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ber_theory = [berawgn(x) for x in EbN0_range] 
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print "Simulating..." 
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ber_simu = [simulate_ber(x) for x in EbN0_range] 
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f = pylab.figure() 
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s = f.add_subplot(1,1,1) 
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s.semilogy(EbN0_range, ber_theory, 'g.', label="Theoretical") 
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s.semilogy(EbN0_range, ber_simu, 'bo', label="Simulated") 
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s.set_title('BER Simulation')

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s.set_xlabel('Eb/N0 (dB)')

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s.set_ylabel('BER')

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s.legend() 
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s.grid() 
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pylab.show() 
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