1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
|
#!/usr/bin/env python
##################################################
# Gnuradio Python Flow Graph
# Title: CPM test
# Author: Achilleas Anastasopoulos
# Description: gnuradio flow graph
# Generated: Thu Feb 19 23:16:23 2009
##################################################
from gnuradio import gr
from gnuradio import trellis
from gnuradio.gr import firdes
from grc_gnuradio import blks2 as grc_blks2
import math
import numpy
import fsm_utils
from gnuradio import trellis
try:
import scipy.stats
except ImportError:
print "Error: Program requires scipy (see: www.scipy.org)."
sys.exit(1)
def run_test(seed,blocksize):
tb = gr.top_block()
##################################################
# Variables
##################################################
M = 2
K = 1
P = 2
h = (1.0*K)/P
L = 3
Q = 4
frac = 0.99
f = trellis.fsm(P,M,L)
# CPFSK signals
#p = numpy.ones(Q)/(2.0)
#q = numpy.cumsum(p)/(1.0*Q)
# GMSK signals
BT=0.3;
tt=numpy.arange(0,L*Q)/(1.0*Q)-L/2.0;
#print tt
p=(0.5*scipy.stats.erfc(2*math.pi*BT*(tt-0.5)/math.sqrt(math.log(2.0))/math.sqrt(2.0))-0.5*scipy.stats.erfc(2*math.pi*BT*(tt+0.5)/math.sqrt(math.log(2.0))/math.sqrt(2.0)))/2.0;
p=p/sum(p)*Q/2.0;
#print p
q=numpy.cumsum(p)/Q;
q=q/q[-1]/2.0;
#print q
(f0T,SS,S,F,Sf,Ff,N) = fsm_utils.make_cpm_signals(K,P,M,L,q,frac)
#print N
#print Ff
Ffa = numpy.insert(Ff,Q,numpy.zeros(N),axis=0)
#print Ffa
MF = numpy.fliplr(numpy.transpose(Ffa))
#print MF
E = numpy.sum(numpy.abs(Sf)**2,axis=0)
Es = numpy.sum(E)/f.O()
#print Es
constellation = numpy.reshape(numpy.transpose(Sf),N*f.O())
#print Ff
#print Sf
#print constellation
#print numpy.max(numpy.abs(SS - numpy.dot(Ff , Sf)))
EsN0_db = 10.0
N0 = Es * 10.0**(-(1.0*EsN0_db)/10.0)
#N0 = 0.0
#print N0
head = 4
tail = 4
numpy.random.seed(seed*666)
data = numpy.random.randint(0, M, head+blocksize+tail+1)
#data = numpy.zeros(blocksize+1+head+tail,'int')
for i in range(head):
data[i]=0
for i in range(tail+1):
data[-i]=0
##################################################
# Blocks
##################################################
random_source_x_0 = gr.vector_source_b(data.tolist(), False)
gr_chunks_to_symbols_xx_0 = gr.chunks_to_symbols_bf((-1, 1), 1)
gr_interp_fir_filter_xxx_0 = gr.interp_fir_filter_fff(Q, p)
gr_frequency_modulator_fc_0 = gr.frequency_modulator_fc(2*math.pi*h*(1.0/Q))
gr_add_vxx_0 = gr.add_vcc(1)
gr_noise_source_x_0 = gr.noise_source_c(gr.GR_GAUSSIAN, (N0/2.0)**0.5, -long(seed))
gr_multiply_vxx_0 = gr.multiply_vcc(1)
gr_sig_source_x_0 = gr.sig_source_c(Q, gr.GR_COS_WAVE, -f0T, 1, 0)
# only works for N=2, do it manually for N>2...
gr_fir_filter_xxx_0_0 = gr.fir_filter_ccc(Q, MF[0].conjugate())
gr_fir_filter_xxx_0_0_0 = gr.fir_filter_ccc(Q, MF[1].conjugate())
gr_streams_to_stream_0 = gr.streams_to_stream(gr.sizeof_gr_complex*1, int(N))
gr_skiphead_0 = gr.skiphead(gr.sizeof_gr_complex*1, int(N*(1+0)))
viterbi = trellis.viterbi_combined_cb(f, head+blocksize+tail, 0, -1, int(N), constellation, trellis.TRELLIS_EUCLIDEAN)
gr_vector_sink_x_0 = gr.vector_sink_b()
##################################################
# Connections
##################################################
tb.connect((random_source_x_0, 0), (gr_chunks_to_symbols_xx_0, 0))
tb.connect((gr_chunks_to_symbols_xx_0, 0), (gr_interp_fir_filter_xxx_0, 0))
tb.connect((gr_interp_fir_filter_xxx_0, 0), (gr_frequency_modulator_fc_0, 0))
tb.connect((gr_frequency_modulator_fc_0, 0), (gr_add_vxx_0, 0))
tb.connect((gr_noise_source_x_0, 0), (gr_add_vxx_0, 1))
tb.connect((gr_add_vxx_0, 0), (gr_multiply_vxx_0, 0))
tb.connect((gr_sig_source_x_0, 0), (gr_multiply_vxx_0, 1))
tb.connect((gr_multiply_vxx_0, 0), (gr_fir_filter_xxx_0_0, 0))
tb.connect((gr_multiply_vxx_0, 0), (gr_fir_filter_xxx_0_0_0, 0))
tb.connect((gr_fir_filter_xxx_0_0, 0), (gr_streams_to_stream_0, 0))
tb.connect((gr_fir_filter_xxx_0_0_0, 0), (gr_streams_to_stream_0, 1))
tb.connect((gr_streams_to_stream_0, 0), (gr_skiphead_0, 0))
tb.connect((gr_skiphead_0, 0), (viterbi, 0))
tb.connect((viterbi, 0), (gr_vector_sink_x_0, 0))
tb.run()
dataest = gr_vector_sink_x_0.data()
#print data
#print numpy.array(dataest)
perr = 0
err = 0
for i in range(blocksize):
if data[head+i] != dataest[head+i]:
#print i
err += 1
if err != 0 :
perr = 1
return (err,perr)
if __name__ == '__main__':
blocksize = 1000
ss=0
ee=0
for i in range(10000):
(s,e) = run_test(i,blocksize)
ss += s
ee += e
if (i+1) % 100 == 0:
print i+1,ss,ee,(1.0*ss)/(i+1)/(1.0*blocksize),(1.0*ee)/(i+1)
print i+1,ss,ee,(1.0*ss)/(i+1)/(1.0*blocksize),(1.0*ee)/(i+1)
|
