def test_002(self):
vlen = 256
subc = 208
L = 8
cplen = 12
blocklen = vlen + cplen
framelength = 11
bits_per_subc = [2] * vlen
data_blocks = 10
N = int(1e9)
# GI metric
pre0, fd = morellimengali_designer.create(subc, vlen, L)
ofdm_frames = \
ofdm_frame_src( vlen, data_blocks, pre0, cplen, framelength,
bits_per_subc )
#uut = ofdm.autocorrelator0( vlen, cplen )
uut = autocorrelator(vlen, cplen)
limit_stream = gr.head(gr.sizeof_float, N)
self.tb.connect(ofdm_frames, uut, limit_stream,
gr.null_sink(gr.sizeof_float))
# log_to_file( self.tb, limit_stream, "data/cyclicprefix_autocorr.float" )
r = time_it(self.tb)
print "Expected throughput: %s Samples/s" % (eng_notation.num_to_str(
float(N) / r))
def __init__( self, vlen ):
gr.hier_block2.__init__( self,
"channel_estimator_001",
gr.io_signature( 1, 1, gr.sizeof_gr_complex * vlen ),
gr.io_signature( 1, 1, gr.sizeof_float ) )
#nondata_blocks = [1]
#pilot_tones = [0]
if snr_estimator_001.vlen != vlen or \
snr_estimator_001.td is None or \
snr_estimator_001.fd is None :
td,fd = morellimengali_designer.create(vlen, vlen,8) #-> replace the fixed number of periodic parts->8
snr_estimator_001.td, snr_estimator_001.fd = td,fd
snr_estimator_001.vlen = vlen
else:
td,fd = snr_estimator_001.td, snr_estimator_001.fd
self.preamble_td = td
self.preamble_fd = fd
uut = milans_snr_estimator( vlen, vlen, 8 )#-> replace the fixed number of periodic parts->8
#uut.set_known_block( 0, self.preamble_fd, True )
#trigger = gr.vector_source_b( [1], True )
self.connect( self, uut )
#self.connect( trigger, ( uut, 1 ) )
self.connect( ( uut), self )
def test_002(self):
vlen = 256
subc = 208
L = 8
cplen = 12
blocklen = vlen + cplen
framelength = 11
bits_per_subc = [2]*vlen
data_blocks = 10
N = int( 1e9 )
# GI metric
pre0,fd = morellimengali_designer.create( subc, vlen, L )
ofdm_frames = \
ofdm_frame_src( vlen, data_blocks, pre0, cplen, framelength,
bits_per_subc )
#uut = ofdm.autocorrelator0( vlen, cplen )
uut = autocorrelator( vlen, cplen )
limit_stream = gr.head( gr.sizeof_float, N )
self.tb.connect( ofdm_frames, uut, limit_stream,
gr.null_sink( gr.sizeof_float ) )
# log_to_file( self.tb, limit_stream, "data/cyclicprefix_autocorr.float" )
r = time_it( self.tb )
print "Expected throughput: %s Samples/s" % ( eng_notation.num_to_str( float(N) / r ) )
def test_101(self):
vlen = 256
subc = 208
L = 8
cplen = 12
blocklen = vlen + cplen
framelength = 11
bits_per_subc = [2]*vlen
data_blocks = 10
# SC metric, ofdm frames generated statically
M = 10
Nsamples_per_frame = framelength * (vlen+cplen)
N = int( 1e9 )
pre0,fd = morellimengali_designer.create( subc, vlen, L )
ofdm_frames = \
ofdm_frame_src( vlen, data_blocks, pre0, cplen, framelength,
bits_per_subc )
dst = gr.vector_sink_c()
#uut = ofdm.autocorrelator( vlen, cplen )
uut = autocorrelator( vlen, cplen )
limit_stream = gr.head( gr.sizeof_gr_complex, Nsamples_per_frame * M )
tb = gr.top_block("s")
tb.connect( ofdm_frames, limit_stream, dst )
tb.run()
src = gr.vector_source_c( dst.data(), True )
limit = gr.head( gr.sizeof_gr_complex, N )
self.tb.connect( src, limit, uut,
gr.null_sink( gr.sizeof_float ) )
r = time_it( self.tb )
print "GI rate: %s Samples/second" \
% ( eng_notation.num_to_str( float(N) / r ) )
def test_101(self):
vlen = 256
subc = 208
L = 8
cplen = 12
blocklen = vlen + cplen
framelength = 11
bits_per_subc = [2] * vlen
data_blocks = 10
# SC metric, ofdm frames generated statically
M = 10
Nsamples_per_frame = framelength * (vlen + cplen)
N = int(1e9)
pre0, fd = morellimengali_designer.create(subc, vlen, L)
ofdm_frames = \
ofdm_frame_src( vlen, data_blocks, pre0, cplen, framelength,
bits_per_subc )
dst = gr.vector_sink_c()
#uut = ofdm.autocorrelator( vlen, cplen )
uut = autocorrelator(vlen, cplen)
limit_stream = gr.head(gr.sizeof_gr_complex, Nsamples_per_frame * M)
tb = gr.top_block("s")
tb.connect(ofdm_frames, limit_stream, dst)
tb.run()
src = gr.vector_source_c(dst.data(), True)
limit = gr.head(gr.sizeof_gr_complex, N)
self.tb.connect(src, limit, uut, gr.null_sink(gr.sizeof_float))
r = time_it(self.tb)
print "GI rate: %s Samples/second" \
% ( eng_notation.num_to_str( float(N) / r ) )
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 )
