def __init__ ( self, fft_length ):
gr.hier_block2.__init__(self, "recursive_timing_metric",
gr.io_signature(1,1,gr.sizeof_gr_complex),
gr.io_signature(1,1,gr.sizeof_float))
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.connect(self, self.input)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
mix_delay = delay(gr.sizeof_gr_complex,fft_length/2+1)
mix_diff = gr.sub_cc()
nominator = accumulator_cc()
inpdelay = delay(gr.sizeof_gr_complex,fft_length/2)
self.connect(self.input, inpdelay,
conj, (mixer,0))
self.connect(self.input, (mixer,1))
self.connect(mixer,(mix_diff,0))
self.connect(mixer, mix_delay, (mix_diff,1))
self.connect(mix_diff,nominator)
rmagsqrd = gr.complex_to_mag_squared()
rm_delay = delay(gr.sizeof_float,fft_length+1)
rm_diff = gr.sub_ff()
denom = accumulator_ff()
self.connect(self.input,rmagsqrd,rm_diff,gr.multiply_const_ff(0.5),denom)
self.connect(rmagsqrd,rm_delay,(rm_diff,1))
ps = gr.complex_to_mag_squared()
rs = gr.multiply_ff()
self.connect(nominator,ps)
self.connect(denom,rs)
self.connect(denom,(rs,1))
div = gr.divide_ff()
self.connect(ps,div)
self.connect(rs,(div,1))
self.connect(div,self)
def __init__(self, fft_length):
gr.hier_block2.__init__(self, "recursive_timing_metric",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.connect(self, self.input)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
mix_delay = delay(gr.sizeof_gr_complex, fft_length / 2 + 1)
mix_diff = gr.sub_cc()
nominator = accumulator_cc()
inpdelay = delay(gr.sizeof_gr_complex, fft_length / 2)
self.connect(self.input, inpdelay, conj, (mixer, 0))
self.connect(self.input, (mixer, 1))
self.connect(mixer, (mix_diff, 0))
self.connect(mixer, mix_delay, (mix_diff, 1))
self.connect(mix_diff, nominator)
rmagsqrd = gr.complex_to_mag_squared()
rm_delay = delay(gr.sizeof_float, fft_length + 1)
rm_diff = gr.sub_ff()
denom = accumulator_ff()
self.connect(self.input, rmagsqrd, rm_diff, gr.multiply_const_ff(0.5),
denom)
self.connect(rmagsqrd, rm_delay, (rm_diff, 1))
ps = gr.complex_to_mag_squared()
rs = gr.multiply_ff()
self.connect(nominator, ps)
self.connect(denom, rs)
self.connect(denom, (rs, 1))
div = gr.divide_ff()
self.connect(ps, div)
self.connect(rs, (div, 1))
self.connect(div, self)
def test_symbol_src ( self, arity ):
vlen = 1
N = int( 1e7 )
demapper = ofdm.generic_demapper_vcb( vlen )
const = demapper.get_constellation( arity )
assert( len( const ) == 2**arity )
symsrc = ofdm.symbol_random_src( const, vlen )
# tx = transmitter_hier_bc(M=M,K=K,qam_size=qam_size,syms_per_frame=syms_per_frame,theta_sel=theta_sel,exclude_preamble=exclude_preamble,sel_preamble=None)
acc = ofdm.accumulator_cc()
skiphead = blocks.skiphead( gr.sizeof_gr_complex, N-1 )
limit = blocks.head( gr.sizeof_gr_complex, 1 )
dst = blocks.vector_sink_c()
c2mag = blocks.complex_to_mag_squared()
acc_c2m = ofdm.accumulator_ff()
skiphead_c2m = blocks.skiphead( gr.sizeof_float, N-1 )
limit_c2m = blocks.head( gr.sizeof_float, 1 )
dst_c2m = blocks.vector_sink_f()
tb = gr.top_block ( "test__block" )
tb.connect( symsrc, acc, skiphead, limit, dst )
tb.connect( symsrc, c2mag, acc_c2m, skiphead_c2m, limit_c2m, dst_c2m )
tb.run()
data = numpy.array( dst.data() )
data_c2m = numpy.array( dst_c2m.data() )
m = data / N
av_pow = data_c2m / N
assert( abs( m ) < 0.01 )
assert( abs( 1.0 - av_pow ) < 0.5 )
print "Uniform distributed random symbol source has"
print "\tno offset for N=%d, relative error: %f" % (arity, abs( m ) )
print "\tAverage signal power equal 1.0, relative error: %f\t\tOK" \
% ( abs( 1.0 - av_pow ) )
