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   1 #!/usr/bin/env python
   2 #
   3 # Copyright 2007,2008 Free Software Foundation, Inc.
   4 #
   5 # This file is part of GNU Radio
   6 #
   7 # GNU Radio is free software; you can redistribute it and/or modify
   8 # it under the terms of the GNU General Public License as published by
   9 # the Free Software Foundation; either version 3, or (at your option)
  10 # any later version.
  11 #
  12 # GNU Radio is distributed in the hope that it will be useful,
  13 # but WITHOUT ANY WARRANTY; without even the implied warranty of
  14 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  15 # GNU General Public License for more details.
  16 #
  17 # You should have received a copy of the GNU General Public License
  18 # along with GNU Radio; see the file COPYING.  If not, write to
  19 # the Free Software Foundation, Inc., 51 Franklin Street,
  20 # Boston, MA 02110-1301, USA.
  21 #
  22
  23 import math
  24 from numpy import fft
  25 from gnuradio import gr
  26
  27 class ofdm_sync_pn(gr.hier_block2):
  28     def __init__(self, fft_length, cp_length, logging=False):
  29         """
  30         OFDM synchronization using PN Correlation:
  31         T. M. Schmidl and D. C. Cox, "Robust Frequency and Timing
  32         Synchonization for OFDM," IEEE Trans. Communications, vol. 45,
  33         no. 12, 1997.
  34         """
  35
  36         gr.hier_block2.__init__(self, "ofdm_sync_pn",
  37                                 gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
  38                                 gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
  39
  40         self.input = gr.add_const_cc(0)
  41
  42         # PN Sync
  43
  44         # Create a delay line
  45         self.delay = gr.delay(gr.sizeof_gr_complex, fft_length/2)
  46
  47         # Correlation from ML Sync
  48         self.conjg = gr.conjugate_cc();
  49         self.corr = gr.multiply_cc();
  50
  51         # Create a moving sum filter for the corr output
  52         if 1:
  53             moving_sum_taps = [1.0 for i in range(fft_length//2)]
  54             self.moving_sum_filter = gr.fir_filter_ccf(1,moving_sum_taps)
  55         else:
  56             moving_sum_taps = [complex(1.0,0.0) for i in range(fft_length//2)]
  57             self.moving_sum_filter = gr.fft_filter_ccc(1,moving_sum_taps)
  58
  59         # Create a moving sum filter for the input
  60         self.inputmag2 = gr.complex_to_mag_squared()
  61         movingsum2_taps = [1.0 for i in range(fft_length//2)]
  62
  63         if 1:
  64             self.inputmovingsum = gr.fir_filter_fff(1,movingsum2_taps)
  65         else:
  66             self.inputmovingsum = gr.fft_filter_fff(1,movingsum2_taps)
  67
  68         self.square = gr.multiply_ff()
  69         self.normalize = gr.divide_ff()
  70
  71         # Get magnitude (peaks) and angle (phase/freq error)
  72         self.c2mag = gr.complex_to_mag_squared()
  73         self.angle = gr.complex_to_arg()
  74
  75         self.sample_and_hold = gr.sample_and_hold_ff()
  76
  77         #ML measurements input to sampler block and detect
  78         self.sub1 = gr.add_const_ff(-1)
  79         self.pk_detect = gr.peak_detector_fb(0.20, 0.20, 30, 0.001)
  80         #self.pk_detect = gr.peak_detector2_fb(9)
  81
  82         self.connect(self, self.input)
  83
  84         # Calculate the frequency offset from the correlation of the preamble
  85         self.connect(self.input, self.delay)
  86         self.connect(self.input, (self.corr,0))
  87         self.connect(self.delay, self.conjg)
  88         self.connect(self.conjg, (self.corr,1))
  89         self.connect(self.corr, self.moving_sum_filter)
  90         self.connect(self.moving_sum_filter, self.c2mag)
  91         self.connect(self.moving_sum_filter, self.angle)
  92         self.connect(self.angle, (self.sample_and_hold,0))
  93
  94         # Get the power of the input signal to normalize the output of the correlation
  95         self.connect(self.input, self.inputmag2, self.inputmovingsum)
  96         self.connect(self.inputmovingsum, (self.square,0))
  97         self.connect(self.inputmovingsum, (self.square,1))
  98         self.connect(self.square, (self.normalize,1))
  99         self.connect(self.c2mag, (self.normalize,0))
 100
 101         # Create a moving sum filter for the corr output
 102         matched_filter_taps = [1.0/cp_length for i in range(cp_length)]
 103         self.matched_filter = gr.fir_filter_fff(1,matched_filter_taps)
 104         self.connect(self.normalize, self.matched_filter)
 105
 106         self.connect(self.matched_filter, self.sub1, self.pk_detect)
 107         #self.connect(self.matched_filter, self.pk_detect)
 108         self.connect(self.pk_detect, (self.sample_and_hold,1))
 109
 110         # Set output signals
 111         #    Output 0: fine frequency correction value
 112         #    Output 1: timing signal
 113         self.connect(self.sample_and_hold, (self,0))
 114         self.connect(self.pk_detect, (self,1))
 115
 116         if logging:
 117             self.connect(self.matched_filter, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-mf_f.dat"))
 118             self.connect(self.normalize, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-theta_f.dat"))
 119             self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-epsilon_f.dat"))
 120             self.connect(self.pk_detect, gr.file_sink(gr.sizeof_char, "ofdm_sync_pn-peaks_b.dat"))
 121             self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-sample_and_hold_f.dat"))
 122             self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pn-input_c.dat"))
 123