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-rw-r--r--gr-digital/python/ofdm_sync_ml.py36
1 files changed, 23 insertions, 13 deletions
diff --git a/gr-digital/python/ofdm_sync_ml.py b/gr-digital/python/ofdm_sync_ml.py
index 7c75d7f1d4..885531f66a 100644
--- a/gr-digital/python/ofdm_sync_ml.py
+++ b/gr-digital/python/ofdm_sync_ml.py
@@ -23,6 +23,16 @@
import math
from gnuradio import gr
+try:
+ from gnuradio import filter
+except ImportError:
+ import filter_swig as filter
+
+try:
+ from gnuradio import blocks
+except ImportError:
+ import blocks_swig as blocks
+
class ofdm_sync_ml(gr.hier_block2):
def __init__(self, fft_length, cp_length, snr, kstime, logging):
''' Maximum Likelihood OFDM synchronizer:
@@ -35,7 +45,7 @@ class ofdm_sync_ml(gr.hier_block2):
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
- self.input = gr.add_const_cc(0)
+ self.input = blocks.add_const_cc(0)
SNR = 10.0**(snr/10.0)
rho = SNR / (SNR + 1.0)
@@ -54,10 +64,10 @@ class ofdm_sync_ml(gr.hier_block2):
# magnitude squared blocks
self.magsqrd1 = gr.complex_to_mag_squared()
self.magsqrd2 = gr.complex_to_mag_squared()
- self.adder = gr.add_ff()
+ self.adder = blocks.add_ff()
moving_sum_taps = [rho/2 for i in range(cp_length)]
- self.moving_sum_filter = gr.fir_filter_fff(1,moving_sum_taps)
+ self.moving_sum_filter = filter.fir_filter_fff(1,moving_sum_taps)
self.connect(self.input,self.magsqrd1)
self.connect(self.delay,self.magsqrd2)
@@ -67,11 +77,11 @@ class ofdm_sync_ml(gr.hier_block2):
# Correlation from ML Sync
- self.conjg = gr.conjugate_cc();
- self.mixer = gr.multiply_cc();
+ self.conjg = blocks.conjugate_cc();
+ self.mixer = blocks.multiply_cc();
movingsum2_taps = [1.0 for i in range(cp_length)]
- self.movingsum2 = gr.fir_filter_ccf(1,movingsum2_taps)
+ self.movingsum2 = filter.fir_filter_ccf(1,movingsum2_taps)
# Correlator data handler
self.c2mag = gr.complex_to_mag()
@@ -82,12 +92,12 @@ class ofdm_sync_ml(gr.hier_block2):
self.connect(self.movingsum2,self.angle)
# ML Sync output arg, need to find maximum point of this
- self.diff = gr.sub_ff()
+ self.diff = blocks.sub_ff()
self.connect(self.c2mag,(self.diff,0))
self.connect(self.moving_sum_filter,(self.diff,1))
#ML measurements input to sampler block and detect
- self.f2c = gr.float_to_complex()
+ self.f2c = blocks.float_to_complex()
self.pk_detect = gr.peak_detector_fb(0.2, 0.25, 30, 0.0005)
self.sample_and_hold = gr.sample_and_hold_ff()
@@ -115,9 +125,9 @@ class ofdm_sync_ml(gr.hier_block2):
# to readjust the timing in the middle of the packet or we ruin the equalizer settings.
kstime = [k.conjugate() for k in kstime]
kstime.reverse()
- self.kscorr = gr.fir_filter_ccc(1, kstime)
+ self.kscorr = filter.fir_filter_ccc(1, kstime)
self.corrmag = gr.complex_to_mag_squared()
- self.div = gr.divide_ff()
+ self.div = blocks.divide_ff()
# The output signature of the correlation has a few spikes because the rest of the
# system uses the repeated preamble symbol. It needs to work that generically if
@@ -126,9 +136,9 @@ class ofdm_sync_ml(gr.hier_block2):
# identify the proper peak and remove other products in this cross-correlation
self.threshold_factor = 0.1
self.slice = gr.threshold_ff(self.threshold_factor, self.threshold_factor, 0)
- self.f2b = gr.float_to_char()
- self.b2f = gr.char_to_float()
- self.mul = gr.multiply_ff()
+ self.f2b = blocks.float_to_char()
+ self.b2f = blocks.char_to_float()
+ self.mul = blocks.multiply_ff()
# Normalize the power of the corr output by the energy. This is not really needed
# and could be removed for performance, but it makes for a cleaner signal.