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Diffstat (limited to 'gr-digital/python/ofdm_sync_pnac.py')
| -rw-r--r-- | gr-digital/python/ofdm_sync_pnac.py | 125 |
1 files changed, 125 insertions, 0 deletions
diff --git a/gr-digital/python/ofdm_sync_pnac.py b/gr-digital/python/ofdm_sync_pnac.py new file mode 100644 index 000000000..10a125964 --- /dev/null +++ b/gr-digital/python/ofdm_sync_pnac.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python +# +# Copyright 2007 Free Software Foundation, Inc. +# +# This file is part of GNU Radio +# +# GNU Radio is free software; you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation; either version 3, or (at your option) +# any later version. +# +# GNU Radio is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with GNU Radio; see the file COPYING. If not, write to +# the Free Software Foundation, Inc., 51 Franklin Street, +# Boston, MA 02110-1301, USA. +# + +import math +from numpy import fft +from gnuradio import gr + +class ofdm_sync_pnac(gr.hier_block2): + def __init__(self, fft_length, cp_length, kstime, logging=False): + """ + OFDM synchronization using PN Correlation and initial cross-correlation: + F. Tufvesson, O. Edfors, and M. Faulkner, "Time and Frequency Synchronization for OFDM using + PN-Sequency Preambles," IEEE Proc. VTC, 1999, pp. 2203-2207. + + This implementation is meant to be a more robust version of the Schmidl and Cox receiver design. + By correlating against the preamble and using that as the input to the time-delayed correlation, + this circuit produces a very clean timing signal at the end of the preamble. The timing is + more accurate and does not have the problem associated with determining the timing from the + plateau structure in the Schmidl and Cox. + + This implementation appears to require that the signal is received with a normalized power or signal + scalling factor to reduce ambiguities intorduced from partial correlation of the cyclic prefix and + the peak detection. A better peak detection block might fix this. + + Also, the cross-correlation falls apart as the frequency offset gets larger and completely fails + when an integer offset is introduced. Another thing to look at. + """ + + gr.hier_block2.__init__(self, "ofdm_sync_pnac", + 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) + + symbol_length = fft_length + cp_length + + # PN Sync with cross-correlation input + + # cross-correlate with the known symbol + kstime = [k.conjugate() for k in kstime[0:fft_length//2]] + kstime.reverse() + self.crosscorr_filter = gr.fir_filter_ccc(1, kstime) + + # Create a delay line + self.delay = gr.delay(gr.sizeof_gr_complex, fft_length/2) + + # Correlation from ML Sync + self.conjg = gr.conjugate_cc(); + self.corr = gr.multiply_cc(); + + # Create a moving sum filter for the input + self.mag = gr.complex_to_mag_squared() + movingsum_taps = (fft_length//1)*[1.0,] + self.power = gr.fir_filter_fff(1,movingsum_taps) + + # Get magnitude (peaks) and angle (phase/freq error) + self.c2mag = gr.complex_to_mag_squared() + self.angle = gr.complex_to_arg() + self.compare = gr.sub_ff() + + self.sample_and_hold = gr.sample_and_hold_ff() + + #ML measurements input to sampler block and detect + self.threshold = gr.threshold_ff(0,0,0) # threshold detection might need to be tweaked + self.peaks = gr.float_to_char() + + self.connect(self, self.input) + + # Cross-correlate input signal with known preamble + self.connect(self.input, self.crosscorr_filter) + + # use the output of the cross-correlation as input time-shifted correlation + self.connect(self.crosscorr_filter, self.delay) + self.connect(self.crosscorr_filter, (self.corr,0)) + self.connect(self.delay, self.conjg) + self.connect(self.conjg, (self.corr,1)) + self.connect(self.corr, self.c2mag) + self.connect(self.corr, self.angle) + self.connect(self.angle, (self.sample_and_hold,0)) + + # Get the power of the input signal to compare against the correlation + self.connect(self.crosscorr_filter, self.mag, self.power) + + # Compare the power to the correlator output to determine timing peak + # When the peak occurs, it peaks above zero, so the thresholder detects this + self.connect(self.c2mag, (self.compare,0)) + self.connect(self.power, (self.compare,1)) + self.connect(self.compare, self.threshold) + self.connect(self.threshold, self.peaks, (self.sample_and_hold,1)) + + # Set output signals + # Output 0: fine frequency correction value + # Output 1: timing signal + self.connect(self.sample_and_hold, (self,0)) + self.connect(self.peaks, (self,1)) + + if logging: + self.connect(self.compare, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-compare_f.dat")) + self.connect(self.c2mag, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-theta_f.dat")) + self.connect(self.power, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-inputpower_f.dat")) + self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-epsilon_f.dat")) + self.connect(self.threshold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-threshold_f.dat")) + self.connect(self.peaks, gr.file_sink(gr.sizeof_char, "ofdm_sync_pnac-peaks_b.dat")) + self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-sample_and_hold_f.dat")) + self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pnac-input_c.dat")) |