def __init__( self, vlen, startup ):
gr.hier_block2.__init__( self,
"vector_acc_se",
gr.io_signature( 1, 1, gr.sizeof_gr_complex * vlen ),
gr.io_signature( 1, 1, gr.sizeof_float ) )
squared_error_subc = gr.complex_to_mag_squared( vlen )
squared_error_block = ofdm.vector_sum_vff( vlen )
accumulated_squared_error = ofdm.accumulator_ff()
if startup > 0:
startup_skip = gr.skiphead( gr.sizeof_gr_complex * vlen, startup )
self.connect( self, startup_skip, squared_error_subc, squared_error_block,
accumulated_squared_error, self )
else:
self.connect( self, squared_error_subc, squared_error_block,
accumulated_squared_error, self )
def __init__( self, vlen, frame_length, no_frames,
no_preambles, bits_per_subc, bitdata_per_frame ):
gr.hier_block2.__init__( self,
"ofdm_ber_estimator",
gr.io_signature( 1, 1, gr.sizeof_gr_complex * vlen ),
gr.io_signature(0,0,0) )
bpsubc = [0] * ( vlen * no_preambles )
assert( len( bits_per_subc ) == vlen )
bpsubc.extend( bits_per_subc )
bits_per_frame = sum( bpsubc ) * ( frame_length - no_preambles )
bm_update_trigger = [0] * frame_length
for i in range( no_preambles + 1 ):
bm_update_trigger[i] = 1
bm_update_trigger = gr.vector_source_b( bm_update_trigger, True )
demapper = ofdm.generic_demapper_vcb( vlen )
bitmap_src = gr.vector_source_b( bpsubc, True, vlen )
data_ref_src = gr.vector_source_b( bitdata_per_frame, True )
compare = gr.xor_bb()
bitstream_c2f = gr.char_to_float()
acc_biterr = ofdm.accumulator_ff()
window_length = bits_per_frame * no_frames
self.N = window_length
dst = limit_stream_get_last_item( acc_biterr, window_length )
self.connect( self, demapper, compare, bitstream_c2f, acc_biterr, dst )
self.connect( bitmap_src, ( demapper, 1 ) )
self.connect( bm_update_trigger, ( demapper, 2 ) )
self.connect( data_ref_src, ( compare, 1 ) )
self.vector_sources = [ bm_update_trigger, bitmap_src ]
self.acc = acc_biterr
self.data_ref_src = data_ref_src
self.compare = compare
self.dst = dst
def sim ( self, arity, snr_db, N ):
vlen = 1
N = int( N )
snr = 10.0**(snr_db/10.0)
sigpow = 1.0
noise_pow = sigpow / snr
demapper = ofdm.generic_demapper_vcb( vlen )
const = demapper.get_constellation( arity )
assert( len( const ) == 2**arity )
symsrc = ofdm.symbol_random_src( const, vlen )
noise_src = ofdm.complex_white_noise( 0.0, sqrt( noise_pow ) )
channel = blocks.add_cc()
bitmap_src = blocks.vector_source_b( [arity] * vlen, True, vlen )
bm_trig_src = blocks.vector_source_b( [1], True )
ref_bitstream = blocks.unpack_k_bits_bb( arity )
bitstream_xor = blocks.xor_bb()
bitstream_c2f = blocks.char_to_float()
acc_biterr = ofdm.accumulator_ff()
skiphead = blocks.skiphead( gr.sizeof_float, N-1 )
limit = blocks.head( gr.sizeof_float, 1 )
dst = blocks.vector_sink_f()
tb = gr.top_block ( "test_block" )
tb.connect( (symsrc,0), (channel,0) )
tb.connect( noise_src, (channel,1) )
tb.connect( channel, (demapper,0), (bitstream_xor,0) )
tb.connect( bitmap_src, (demapper,1) )
tb.connect( bm_trig_src, (demapper,2) )
tb.connect( (symsrc,1), ref_bitstream, (bitstream_xor,1) )
tb.connect( bitstream_xor, bitstream_c2f, acc_biterr )
tb.connect( acc_biterr, skiphead, limit, dst )
tb.run()
bit_errors = numpy.array( dst.data() )
assert( len( bit_errors ) == 1 )
bit_errors = bit_errors[0]
return bit_errors / N
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 ) )
def test_001(self):
vlen = 256
subc = 208
L = 8
cplen = 12
blocklen = vlen + cplen
framelength = 11
bits_per_subc = [2] * vlen
data_blocks = 10
N = int(1e8)
profiling = False
pre0, fd = morellimengali_designer.create(subc, vlen, L)
ofdm_frames = \
ofdm_frame_src( vlen, data_blocks, pre0, cplen, framelength,
bits_per_subc )
uut = autocorrelator(vlen / 2, vlen / 2)
ref = recursive_timing_metric(vlen)
limit_stream = gr.head(gr.sizeof_float, N)
self.tb.connect(ofdm_frames, uut, limit_stream,
gr.null_sink(gr.sizeof_float))
# limit_stream.enable_detailed_profiling( profiling )
# uut.s2.enable_detailed_profiling( profiling )
if not profiling:
limit_stream2 = gr.head(gr.sizeof_float, N)
compare = gr.sub_ff()
err_acc = ofdm.accumulator_ff()
skip_err = gr.skiphead(gr.sizeof_float, N - 1)
last_err_val = gr.head(gr.sizeof_float, 1)
err_sink = gr.vector_sink_f()
self.tb.connect(ofdm_frames, ref, limit_stream2,
gr.null_sink(gr.sizeof_float))
self.tb.connect(uut, (compare, 0))
self.tb.connect(ref, (compare, 1))
self.tb.connect(compare, err_acc)
self.tb.connect(err_acc, skip_err)
self.tb.connect(skip_err, last_err_val)
self.tb.connect(last_err_val, err_sink)
# log_to_file( self.tb, limit_stream, "data/autocorr_uut.float" )
# log_to_file( self.tb, limit_stream2, "data/autocorr_ref.float" )
# r = time_it( self.tb )
self.tb.run()
# print "Expected throughput: %s Samples/s" % ( eng_notation.num_to_str( float(N) / r ) )
if not profiling:
e = numpy.array(err_sink.data())[0]
print "Err: %.7f" % (e)
def test_001(self):
vlen = 256
subc = 208
L = 8
cplen = 12
blocklen = vlen + cplen
framelength = 11
bits_per_subc = [2]*vlen
data_blocks = 10
N = int( 1e8 )
profiling = False
pre0,fd = morellimengali_designer.create( subc, vlen, L )
ofdm_frames = \
ofdm_frame_src( vlen, data_blocks, pre0, cplen, framelength,
bits_per_subc )
uut = autocorrelator( vlen/2, vlen/2 )
ref = recursive_timing_metric( vlen )
limit_stream = gr.head( gr.sizeof_float, N )
self.tb.connect( ofdm_frames, uut, limit_stream,
gr.null_sink( gr.sizeof_float ) )
# limit_stream.enable_detailed_profiling( profiling )
# uut.s2.enable_detailed_profiling( profiling )
if not profiling:
limit_stream2 = gr.head( gr.sizeof_float, N )
compare = gr.sub_ff()
err_acc = ofdm.accumulator_ff()
skip_err = gr.skiphead( gr.sizeof_float, N-1 )
last_err_val = gr.head( gr.sizeof_float, 1 )
err_sink = gr.vector_sink_f()
self.tb.connect( ofdm_frames, ref, limit_stream2,
gr.null_sink( gr.sizeof_float ) )
self.tb.connect( uut, ( compare, 0 ) )
self.tb.connect( ref, ( compare, 1 ) )
self.tb.connect( compare, err_acc )
self.tb.connect( err_acc, skip_err )
self.tb.connect( skip_err, last_err_val )
self.tb.connect( last_err_val, err_sink )
# log_to_file( self.tb, limit_stream, "data/autocorr_uut.float" )
# log_to_file( self.tb, limit_stream2, "data/autocorr_ref.float" )
# r = time_it( self.tb )
self.tb.run()
# print "Expected throughput: %s Samples/s" % ( eng_notation.num_to_str( float(N) / r ) )
if not profiling:
e = numpy.array( err_sink.data() )[0]
print "Err: %.7f" % ( e )
def sim ( self, arity, snr_db, N ):
vlen = 10
N = int( N )
snr = 10.0**(snr_db/10.0)
sigpow = 1.0
noise_pow = sigpow / snr
#skipping first symbol due to demapper implementation (demmaper assumes that the first symbol is ID and do not decode ui)
skiphead_src = blocks.skiphead( gr.sizeof_char, vlen+3)#vlen+3 )
demapper = ofdm.generic_demapper_vcb( vlen,N/vlen+1 )
const = demapper.get_constellation( arity )
assert( len( const ) == 2**arity )
symsrc = ofdm.symbol_random_src( const, vlen )
#noise_src = ofdm.complex_white_noise( 0.0, sqrt( noise_pow ) )
noise_src = analog.fastnoise_source_c(analog.GR_GAUSSIAN, 0.0, 0, 8192)
channel = blocks.add_cc()
ch_model = channels.channel_model(
noise_voltage=0.0,
frequency_offset=0.0,
epsilon=1.0,
#taps = (0.998160541385960,0.0605566335500750,0.00290305927764350),
taps = (1,0),
noise_seed=8192,
block_tags=False
)
bitmap_src = blocks.vector_source_b( [arity] * vlen, True, vlen )
#bm_trig_src = blocks.vector_source_b( [1], True )
ref_bitstream = blocks.unpack_k_bits_bb( arity )
bitstream_xor = blocks.xor_bb()
bitstream_c2f = blocks.char_to_float()
acc_biterr = ofdm.accumulator_ff()
skiphead = blocks.skiphead( gr.sizeof_float, N-1 )
limit = blocks.head( gr.sizeof_float, 1 )
dst = blocks.vector_sink_f()
rec_dst = blocks.vector_sink_b()
ref_dst = blocks.vector_sink_b()
tb = gr.top_block ( "test_block" )
#tb.connect( (symsrc,0),blocks.vector_to_stream(gr.sizeof_gr_complex ,vlen),blocks.head(gr.sizeof_gr_complex,N/arity),blocks.null_sink(gr.sizeof_gr_complex))
#tb.connect( (symsrc,0),fft.fft_vcc(vlen,False,[],True),blocks.vector_to_stream(gr.sizeof_gr_complex ,vlen), ch_model, (channel,0) )
tb.connect( (symsrc,0),blocks.vector_to_stream(gr.sizeof_gr_complex ,vlen), ch_model, (channel,0) )
tb.connect( noise_src, (channel,1) )
#tb.connect( channel, blocks.stream_to_vector(gr.sizeof_gr_complex ,vlen),fft.fft_vcc(vlen,True,[],True), (demapper,0), (bitstream_xor,0) )
tb.connect( channel, blocks.stream_to_vector(gr.sizeof_gr_complex ,vlen), (demapper,0), (bitstream_xor,0) )
tb.connect( bitmap_src, (demapper,1) )
#tb.connect( bm_trig_src, (demapper,2) )
tb.connect( (symsrc,1),blocks.vector_to_stream(gr.sizeof_char ,vlen),skiphead_src, ref_bitstream, (bitstream_xor,1) )
tb.connect( bitstream_xor, bitstream_c2f, acc_biterr )
tb.connect( acc_biterr, skiphead, limit, dst )
tb.connect( demapper, rec_dst )
tb.connect( ref_bitstream, ref_dst )
tb.run()
bit_errors = numpy.array( dst.data() )
assert( len( bit_errors ) == 1 )
bit_errors = bit_errors[0]
rec_data = list(rec_dst.data())
ref_data = list(ref_dst.data())
print "ref_data: ", ref_data[:2000]
print "size ref_data: ", len(ref_data)#[:2320
print "rec_data: ", rec_data[:500]
print "size rec_data: ", len(rec_data)#[:2320
return bit_errors / N