def make_gmsk(samples_per_symbol, BT):
# M, K, P, h, L are fixed for GMSK
M = 2 # number of bits per symbol
K = 1
P = 2 # h=K/P, K and P are relatively prime
h = (1.0 * K) / P
L = 3
Q = samples_per_symbol
frac = 0.99 # fractional energy component deemed necessary to consider in the trellis, for finding dimensionality
fsm = trellis.fsm(P, M, L)
tt = numpy.arange(0, L * Q) / (1.0 * Q) - L / 2.0
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
q = numpy.cumsum(p) / Q
q = q / q[-1] / 2.0
(f0T, SS, S, F, Sf, Ff, N) = fsm_utils.make_cpm_signals(K, P, M, L, q, frac)
constellation = numpy.reshape(numpy.transpose(Sf), N * fsm.O())
Ffa = numpy.insert(Ff, Q, numpy.zeros(N), axis=0)
MF = numpy.fliplr(numpy.transpose(Ffa))
return (fsm, constellation, MF, N, f0T)
def make_gmsk(samples_per_symbol, BT):
#M, K, P, h, L are fixed for GMSK
M = 2 #number of bits per symbol
K = 1
P = 2 #h=K/P, K and P are relatively prime
h = (1.0 * K) / P
L = 3
Q = samples_per_symbol
frac = 0.99 #fractional energy component deemed necessary to consider in the trellis, for finding dimensionality
fsm = trellis.fsm(P, M, L)
tt = numpy.arange(0, L * Q) / (1.0 * Q) - L / 2.0
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
q = numpy.cumsum(p) / Q
q = q / q[-1] / 2.0
(f0T, SS, S, F, Sf, Ff,
N) = fsm_utils.make_cpm_signals(K, P, M, L, q, frac)
constellation = numpy.reshape(numpy.transpose(Sf), N * fsm.O())
Ffa = numpy.insert(Ff, Q, numpy.zeros(N), axis=0)
MF = numpy.fliplr(numpy.transpose(Ffa))
return (fsm, constellation, MF, N, f0T)
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.special.erfc(2*math.pi*BT*(tt-0.5)/math.sqrt(math.log(2.0))/math.sqrt(2.0))-0.5*scipy.special.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 = blocks.vector_source_b(data.tolist(), False)
digital_chunks_to_symbols_xx_0 = digital.chunks_to_symbols_bf((-1, 1), 1)
filter_interp_fir_filter_xxx_0 = filter.interp_fir_filter_fff(Q, p)
analog_frequency_modulator_fc_0 = analog.frequency_modulator_fc(2*math.pi*h*(1.0/Q))
blocks_add_vxx_0 = blocks.add_vcc(1)
analog_noise_source_x_0 = analog.noise_source_c(analog.GR_GAUSSIAN, (N0/2.0)**0.5, -long(seed))
blocks_multiply_vxx_0 = blocks.multiply_vcc(1)
analog_sig_source_x_0 = analog.sig_source_c(Q, analog.GR_COS_WAVE, -f0T, 1, 0)
# only works for N=2, do it manually for N>2...
filter_fir_filter_xxx_0_0 = filter.fir_filter_ccc(Q, MF[0].conjugate())
filter_fir_filter_xxx_0_0_0 = filter.fir_filter_ccc(Q, MF[1].conjugate())
blocks_streams_to_stream_0 = blocks.streams_to_stream(gr.sizeof_gr_complex*1, int(N))
blocks_skiphead_0 = blocks.skiphead(gr.sizeof_gr_complex*1, int(N*(1+0)))
viterbi = trellis.viterbi_combined_cb(f, head+blocksize+tail, 0, -1, int(N),
constellation, digital.TRELLIS_EUCLIDEAN)
blocks_vector_sink_x_0 = blocks.vector_sink_b()
##################################################
# Connections
##################################################
tb.connect((random_source_x_0, 0), (digital_chunks_to_symbols_xx_0, 0))
tb.connect((digital_chunks_to_symbols_xx_0, 0), (filter_interp_fir_filter_xxx_0, 0))
tb.connect((filter_interp_fir_filter_xxx_0, 0), (analog_frequency_modulator_fc_0, 0))
tb.connect((analog_frequency_modulator_fc_0, 0), (blocks_add_vxx_0, 0))
tb.connect((analog_noise_source_x_0, 0), (blocks_add_vxx_0, 1))
tb.connect((blocks_add_vxx_0, 0), (blocks_multiply_vxx_0, 0))
tb.connect((analog_sig_source_x_0, 0), (blocks_multiply_vxx_0, 1))
tb.connect((blocks_multiply_vxx_0, 0), (filter_fir_filter_xxx_0_0, 0))
tb.connect((blocks_multiply_vxx_0, 0), (filter_fir_filter_xxx_0_0_0, 0))
tb.connect((filter_fir_filter_xxx_0_0, 0), (blocks_streams_to_stream_0, 0))
tb.connect((filter_fir_filter_xxx_0_0_0, 0), (blocks_streams_to_stream_0, 1))
tb.connect((blocks_streams_to_stream_0, 0), (blocks_skiphead_0, 0))
tb.connect((blocks_skiphead_0, 0), (viterbi, 0))
tb.connect((viterbi, 0), (blocks_vector_sink_x_0, 0))
tb.run()
dataest = blocks_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)
