def __init__(self, subc, vlen, ss):
gr.hier_block2.__init__(
self,
"new_snr_estimator",
gr.io_signature(2, 2, gr.sizeof_gr_complex * vlen),
#gr.io_signature2(2,2,gr.sizeof_float*vlen,gr.sizeof_float*vlen/ss*(ss-1)))
gr.io_signature(1, 1, gr.sizeof_float * vlen))
print "Created Milan's SINR estimator 2"
config = station_configuration()
# trigger = [0]*vlen
# trigger[0] = 1
#
# v = range (vlen/ss)
# ones_ind= map(lambda z: z*ss,v)
#
# skip2_pr0 = skip(gr.sizeof_gr_complex,vlen)
# skip2_pr1 = skip(gr.sizeof_gr_complex,vlen)
# for x in ones_ind:
# skip2_pr0.skip(x)
# skip2_pr1.skip(x)
#
# #print "skipped ones",ones_ind
#
# v2s_pr0 = gr.vector_to_stream(gr.sizeof_gr_complex,vlen)
# v2s_pr1 = gr.vector_to_stream(gr.sizeof_gr_complex,vlen)
#
# s2v2_pr0 = gr.stream_to_vector(gr.sizeof_gr_complex,vlen/ss*(ss-1))
# trigger_src_2_pr0 = gr.vector_source_b(trigger,True)
# s2v2_pr1 = gr.stream_to_vector(gr.sizeof_gr_complex,vlen/ss*(ss-1))
# trigger_src_2_pr1 = gr.vector_source_b(trigger,True)
#
# mag_sq_zeros_pr0 = gr.complex_to_mag_squared(vlen/ss*(ss-1))
# mag_sq_zeros_pr1 = gr.complex_to_mag_squared(vlen/ss*(ss-1))
#
#
# filt_zeros_pr0 = gr.single_pole_iir_filter_ff(0.01,vlen/ss*(ss-1))
# filt_zeros_pr1 = gr.single_pole_iir_filter_ff(0.01,vlen/ss*(ss-1))
# v1 = vlen/ss*(ss-1)
# vevc1 =[-1]*v1
# neg_nomin_z = gr.multiply_const_vff(vevc1)
# div_z=gr.divide_ff(vlen/ss*(ss-1))
# on_zeros = gr.add_const_vff(vevc1)
# sum_zeros = add_vff(vlen/ss*(ss-1))
#
# For average
#sum_all = vector_sum_vff(vlen)
#mult = blocks.multiply_const_ff(1./vlen)
#scsnr_db_av = blocks.nlog10_ff(10,1,0)
#filt_end_av = filter.single_pole_iir_filter_ff(0.1)
#
#
# self.connect((self,0),v2s_pr0,skip2_pr0,s2v2_pr0,mag_sq_zeros_pr0,filt_zeros_pr0)
# self.connect(trigger_src_2_pr0,(skip2_pr0,1))
#
#
#
# self.connect((self,1),v2s_pr1,skip2_pr1,s2v2_pr1,mag_sq_zeros_pr1,filt_zeros_pr1)
# self.connect(trigger_src_2_pr1,(skip2_pr1,1))
#
#
# # On zeros
# self.connect(filt_zeros_pr1,(sum_zeros,0))
# self.connect(filt_zeros_pr0,neg_nomin_z,(sum_zeros,1))
# self.connect(sum_zeros,div_z)
# self.connect(filt_zeros_pr0,(div_z,1))
estimator = sinr_estimator(vlen, ss, config.dc_null)
scsnr_db = blocks.nlog10_ff(10, vlen, 0)
#filt_end = filter.single_pole_iir_filter_ff(0.1,vlen)
dd = []
#for i in range (vlen/ss):
# dd.extend([i*ss])
for i in range(vlen / ss):
if i < (vlen / ss) / 2:
dd.extend([i * ss + 4 + config.dc_null / 2])
else:
dd.extend([i * ss + 4 - config.dc_null / 2])
#print dd
interpolator = sinr_interpolator(vlen, ss, dd)
self.connect((self, 0), (estimator, 0))
self.connect((self, 1), (estimator, 1))
#self.connect(estimator,interpolator,filt_end,scsnr_db,self)
self.connect(estimator, interpolator, scsnr_db, self)
def __init__(self, subc, vlen, ss):
gr.hier_block2.__init__(self, "new_snr_estimator",
gr.io_signature(2,2,gr.sizeof_gr_complex*vlen),
#gr.io_signature2(2,2,gr.sizeof_float*vlen,gr.sizeof_float*vlen/ss*(ss-1)))
gr.io_signature(1,1,gr.sizeof_float*vlen))
print "Created Milan's SINR estimator 2"
config = station_configuration()
# trigger = [0]*vlen
# trigger[0] = 1
#
# v = range (vlen/ss)
# ones_ind= map(lambda z: z*ss,v)
#
# skip2_pr0 = skip(gr.sizeof_gr_complex,vlen)
# skip2_pr1 = skip(gr.sizeof_gr_complex,vlen)
# for x in ones_ind:
# skip2_pr0.skip(x)
# skip2_pr1.skip(x)
#
# #print "skipped ones",ones_ind
#
# v2s_pr0 = gr.vector_to_stream(gr.sizeof_gr_complex,vlen)
# v2s_pr1 = gr.vector_to_stream(gr.sizeof_gr_complex,vlen)
#
# s2v2_pr0 = gr.stream_to_vector(gr.sizeof_gr_complex,vlen/ss*(ss-1))
# trigger_src_2_pr0 = gr.vector_source_b(trigger,True)
# s2v2_pr1 = gr.stream_to_vector(gr.sizeof_gr_complex,vlen/ss*(ss-1))
# trigger_src_2_pr1 = gr.vector_source_b(trigger,True)
#
# mag_sq_zeros_pr0 = gr.complex_to_mag_squared(vlen/ss*(ss-1))
# mag_sq_zeros_pr1 = gr.complex_to_mag_squared(vlen/ss*(ss-1))
#
#
# filt_zeros_pr0 = gr.single_pole_iir_filter_ff(0.01,vlen/ss*(ss-1))
# filt_zeros_pr1 = gr.single_pole_iir_filter_ff(0.01,vlen/ss*(ss-1))
# v1 = vlen/ss*(ss-1)
# vevc1 =[-1]*v1
# neg_nomin_z = gr.multiply_const_vff(vevc1)
# div_z=gr.divide_ff(vlen/ss*(ss-1))
# on_zeros = gr.add_const_vff(vevc1)
# sum_zeros = add_vff(vlen/ss*(ss-1))
#
# For average
#sum_all = vector_sum_vff(vlen)
#mult = blocks.multiply_const_ff(1./vlen)
#scsnr_db_av = blocks.nlog10_ff(10,1,0)
#filt_end_av = filter.single_pole_iir_filter_ff(0.1)
#
#
# self.connect((self,0),v2s_pr0,skip2_pr0,s2v2_pr0,mag_sq_zeros_pr0,filt_zeros_pr0)
# self.connect(trigger_src_2_pr0,(skip2_pr0,1))
#
#
#
# self.connect((self,1),v2s_pr1,skip2_pr1,s2v2_pr1,mag_sq_zeros_pr1,filt_zeros_pr1)
# self.connect(trigger_src_2_pr1,(skip2_pr1,1))
#
#
# # On zeros
# self.connect(filt_zeros_pr1,(sum_zeros,0))
# self.connect(filt_zeros_pr0,neg_nomin_z,(sum_zeros,1))
# self.connect(sum_zeros,div_z)
# self.connect(filt_zeros_pr0,(div_z,1))
estimator = sinr_estimator(vlen, ss, config.dc_null)
scsnr_db = blocks.nlog10_ff(10,vlen,0)
#filt_end = filter.single_pole_iir_filter_ff(0.1,vlen)
dd = []
#for i in range (vlen/ss):
# dd.extend([i*ss])
for i in range (vlen/ss):
if i < (vlen/ss)/2:
dd.extend([i*ss + 4 + config.dc_null/2])
else:
dd.extend([i*ss + 4 - config.dc_null/2])
#print dd
interpolator = sinr_interpolator(vlen, ss,dd)
self.connect((self,0),(estimator,0))
self.connect((self,1),(estimator,1))
#self.connect(estimator,interpolator,filt_end,scsnr_db,self)
self.connect(estimator,interpolator,scsnr_db,self)
def __init__(self, subc, vlen, ss):
gr.hier_block2.__init__(
self,
"new_snr_estimator",
gr.io_signature(2, 2, gr.sizeof_gr_complex * vlen),
#gr.io_signature2(2,2,gr.sizeof_float*vlen,gr.sizeof_float*vlen/ss*(ss-1)))
gr.io_signature2(2, 2, gr.sizeof_float * vlen, gr.sizeof_float))
print "Created Milan's SINR estimator"
trigger = [0] * vlen
trigger[0] = 1
v = range(vlen / ss)
ones_ind = map(lambda z: z * ss, v)
skip2_pr0 = skip(gr.sizeof_gr_complex, vlen)
skip2_pr1 = skip(gr.sizeof_gr_complex, vlen)
for x in ones_ind:
skip2_pr0.skip(x)
skip2_pr1.skip(x)
#print "skipped ones",ones_ind
v2s_pr0 = gr.vector_to_stream(gr.sizeof_gr_complex, vlen)
v2s_pr1 = gr.vector_to_stream(gr.sizeof_gr_complex, vlen)
s2v2_pr0 = gr.stream_to_vector(gr.sizeof_gr_complex,
vlen / ss * (ss - 1))
trigger_src_2_pr0 = gr.vector_source_b(trigger, True)
s2v2_pr1 = gr.stream_to_vector(gr.sizeof_gr_complex,
vlen / ss * (ss - 1))
trigger_src_2_pr1 = gr.vector_source_b(trigger, True)
mag_sq_zeros_pr0 = gr.complex_to_mag_squared(vlen / ss * (ss - 1))
mag_sq_zeros_pr1 = gr.complex_to_mag_squared(vlen / ss * (ss - 1))
filt_zeros_pr0 = gr.single_pole_iir_filter_ff(0.01,
vlen / ss * (ss - 1))
filt_zeros_pr1 = gr.single_pole_iir_filter_ff(0.01,
vlen / ss * (ss - 1))
v1 = vlen / ss * (ss - 1)
vevc1 = [-1] * v1
neg_nomin_z = gr.multiply_const_vff(vevc1)
div_z = gr.divide_ff(vlen / ss * (ss - 1))
on_zeros = gr.add_const_vff(vevc1)
sum_zeros = add_vff(vlen / ss * (ss - 1))
# For average
sum_all = vector_sum_vff(vlen)
mult = gr.multiply_const_ff(1. / vlen)
scsnr_db_av = gr.nlog10_ff(10, 1, 0)
filt_end_av = gr.single_pole_iir_filter_ff(0.1)
self.connect((self, 0), v2s_pr0, skip2_pr0, s2v2_pr0, mag_sq_zeros_pr0,
filt_zeros_pr0)
self.connect(trigger_src_2_pr0, (skip2_pr0, 1))
self.connect((self, 1), v2s_pr1, skip2_pr1, s2v2_pr1, mag_sq_zeros_pr1,
filt_zeros_pr1)
self.connect(trigger_src_2_pr1, (skip2_pr1, 1))
# On zeros
self.connect(filt_zeros_pr1, (sum_zeros, 0))
self.connect(filt_zeros_pr0, neg_nomin_z, (sum_zeros, 1))
self.connect(sum_zeros, div_z)
self.connect(filt_zeros_pr0, (div_z, 1))
scsnr_db = gr.nlog10_ff(10, vlen, 0)
filt_end = gr.single_pole_iir_filter_ff(0.1, vlen)
dd = []
for i in range(vlen / ss):
dd.extend([i * ss])
#print dd
interpolator = sinr_interpolator(vlen, ss, dd)
self.connect(div_z, interpolator, filt_end, scsnr_db, self)
self.connect(interpolator, sum_all, mult, scsnr_db_av, filt_end_av,
(self, 1))
def __init__(self, subc, vlen, ss):
gr.hier_block2.__init__(self, "new_snr_estimator",
gr.io_signature(2,2,gr.sizeof_gr_complex*vlen),
#gr.io_signature2(2,2,gr.sizeof_float*vlen,gr.sizeof_float*vlen/ss*(ss-1)))
gr.io_signature2(2,2,gr.sizeof_float*vlen,gr.sizeof_float))
print "Created Milan's SINR estimator"
trigger = [0]*vlen
trigger[0] = 1
v = range (vlen/ss)
ones_ind= map(lambda z: z*ss,v)
skip2_pr0 = skip(gr.sizeof_gr_complex,vlen)
skip2_pr1 = skip(gr.sizeof_gr_complex,vlen)
for x in ones_ind:
skip2_pr0.skip(x)
skip2_pr1.skip(x)
#print "skipped ones",ones_ind
v2s_pr0 = gr.vector_to_stream(gr.sizeof_gr_complex,vlen)
v2s_pr1 = gr.vector_to_stream(gr.sizeof_gr_complex,vlen)
s2v2_pr0 = gr.stream_to_vector(gr.sizeof_gr_complex,vlen/ss*(ss-1))
trigger_src_2_pr0 = gr.vector_source_b(trigger,True)
s2v2_pr1 = gr.stream_to_vector(gr.sizeof_gr_complex,vlen/ss*(ss-1))
trigger_src_2_pr1 = gr.vector_source_b(trigger,True)
mag_sq_zeros_pr0 = gr.complex_to_mag_squared(vlen/ss*(ss-1))
mag_sq_zeros_pr1 = gr.complex_to_mag_squared(vlen/ss*(ss-1))
filt_zeros_pr0 = gr.single_pole_iir_filter_ff(0.01,vlen/ss*(ss-1))
filt_zeros_pr1 = gr.single_pole_iir_filter_ff(0.01,vlen/ss*(ss-1))
v1 = vlen/ss*(ss-1)
vevc1 =[-1]*v1
neg_nomin_z = gr.multiply_const_vff(vevc1)
div_z=gr.divide_ff(vlen/ss*(ss-1))
on_zeros = gr.add_const_vff(vevc1)
sum_zeros = add_vff(vlen/ss*(ss-1))
# For average
sum_all = vector_sum_vff(vlen)
mult = gr.multiply_const_ff(1./vlen)
scsnr_db_av = gr.nlog10_ff(10,1,0)
filt_end_av = gr.single_pole_iir_filter_ff(0.1)
self.connect((self,0),v2s_pr0,skip2_pr0,s2v2_pr0,mag_sq_zeros_pr0,filt_zeros_pr0)
self.connect(trigger_src_2_pr0,(skip2_pr0,1))
self.connect((self,1),v2s_pr1,skip2_pr1,s2v2_pr1,mag_sq_zeros_pr1,filt_zeros_pr1)
self.connect(trigger_src_2_pr1,(skip2_pr1,1))
# On zeros
self.connect(filt_zeros_pr1,(sum_zeros,0))
self.connect(filt_zeros_pr0,neg_nomin_z,(sum_zeros,1))
self.connect(sum_zeros,div_z)
self.connect(filt_zeros_pr0,(div_z,1))
scsnr_db = gr.nlog10_ff(10,vlen,0)
filt_end = gr.single_pole_iir_filter_ff(0.1,vlen)
dd = []
for i in range (vlen/ss):
dd.extend([i*ss])
#print dd
interpolator = sinr_interpolator(vlen, ss,dd)
self.connect(div_z,interpolator,filt_end,scsnr_db,self)
self.connect(interpolator,sum_all,mult,scsnr_db_av,filt_end_av,(self,1))
