diff options
Diffstat (limited to 'gr-digital/python/digital/ofdm_sync_pnac.py')
| -rw-r--r-- | gr-digital/python/digital/ofdm_sync_pnac.py | 126 |
1 files changed, 0 insertions, 126 deletions
diff --git a/gr-digital/python/digital/ofdm_sync_pnac.py b/gr-digital/python/digital/ofdm_sync_pnac.py deleted file mode 100644 index 4d756f7ff7..0000000000 --- a/gr-digital/python/digital/ofdm_sync_pnac.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/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. -# - -from __future__ import division -from __future__ import unicode_literals - - -from gnuradio import gr, blocks, filter - - -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 - scaling factor to reduce ambiguities introduced 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 = blocks.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 = filter.fir_filter_ccc(1, kstime) - - # Create a delay line - self.delay = blocks.delay(gr.sizeof_gr_complex, fft_length / 2) - - # Correlation from ML Sync - self.conjg = blocks.conjugate_cc(); - self.corr = blocks.multiply_cc(); - - # Create a moving sum filter for the input - self.mag = blocks.complex_to_mag_squared() - self.power = filter.fir_filter_fff(1, [1.0] * int(fft_length)) - - # Get magnitude (peaks) and angle (phase/freq error) - self.c2mag = blocks.complex_to_mag_squared() - self.angle = blocks.complex_to_arg() - self.compare = blocks.sub_ff() - - self.sample_and_hold = blocks.sample_and_hold_ff() - - #ML measurements input to sampler block and detect - self.threshold = blocks.threshold_ff(0,0,0) # threshold detection might need to be tweaked - self.peaks = blocks.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, blocks.file_sink(gr.sizeof_float, "ofdm_sync_pnac-compare_f.dat")) - self.connect(self.c2mag, blocks.file_sink(gr.sizeof_float, "ofdm_sync_pnac-theta_f.dat")) - self.connect(self.power, blocks.file_sink(gr.sizeof_float, "ofdm_sync_pnac-inputpower_f.dat")) - self.connect(self.angle, blocks.file_sink(gr.sizeof_float, "ofdm_sync_pnac-epsilon_f.dat")) - self.connect(self.threshold, blocks.file_sink(gr.sizeof_float, "ofdm_sync_pnac-threshold_f.dat")) - self.connect(self.peaks, blocks.file_sink(gr.sizeof_char, "ofdm_sync_pnac-peaks_b.dat")) - self.connect(self.sample_and_hold, blocks.file_sink(gr.sizeof_float, "ofdm_sync_pnac-sample_and_hold_f.dat")) - self.connect(self.input, blocks.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pnac-input_c.dat")) |