mirror of
https://github.com/hb9fxq/gr-digitalhf
synced 2024-11-05 05:55:53 +00:00
fix for broken rotator::rotateN (VOLK)/ minor API change
This commit is contained in:
Christoph Mayer
parent
f1f3708dfa
commit
d10b886474
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@ -71,8 +71,6 @@ adaptive_dfe_impl::adaptive_dfe_impl(int sps, // samples per symbol
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, _mu(mu)
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, _alpha(alpha)
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, _use_symbol_taps(true)
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// , _py_module_name(python_module_name)
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// , _physicalLayer()
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, _taps_samples(nullptr)
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, _taps_symbols(nullptr)
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, _hist_symbols(nullptr)
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@ -95,7 +93,7 @@ adaptive_dfe_impl::adaptive_dfe_impl(int sps, // samples per symbol
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, _num_samples_since_filter_update(0)
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, _rotated_samples()
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, _rotator()
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, _control_loop(2*M_PI/100, 5e-5, -5e-5)
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, _control_loop(2*M_PI/100, 5e-2, -5e-2)
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{
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GR_LOG_DECLARE_LOGPTR(d_logger);
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GR_LOG_ASSIGN_LOGPTR(d_logger, "adaptive_dfe");
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@ -136,7 +134,6 @@ adaptive_dfe_impl::general_work(int noutput_items,
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gr_vector_void_star &output_items)
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{
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gr::thread::scoped_lock lock(d_setlock);
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//GR_LOG_DEBUG(d_logger, str(boost::format("work: %d") % noutput_items));
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gr_complex const* in = (gr_complex const *)input_items[0];
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gr_complex *out_symb = (gr_complex *)output_items[0];
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gr_complex *out_taps = (gr_complex *)output_items[1];
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@ -149,7 +146,6 @@ adaptive_dfe_impl::general_work(int noutput_items,
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return 0;
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int const ninput = ninput_items[0] - _nGuard - _nF;
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int nout = 0; // counter for produced output items
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switch (_state) {
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case WAIT_FOR_PREAMBLE: {
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@ -159,13 +155,6 @@ adaptive_dfe_impl::general_work(int noutput_items,
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consume(0, ninput - history()+1);
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} else {
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tag_t const& tag = v.front();
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// uint64_t const offset = tag.offset - nitems_read(0) + history() - 1;
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// std::cout << "========= offset= " << offset
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// << " tag.offset= " << tag.offset
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// << " nitems_read(0)= " << nitems_read(0)
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// << " tag.offset-nitems_read(0)= " << tag.offset - nitems_read(0) << " ==========" << std::endl;
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// for (int k=0; k<ninput; ++k)
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// std::cout << "SAMPLE: " << k << " " << k-int(offset) << " " << k+nitems_read(0) << " " << in[k] << std::endl;
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reset_filter();
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_descrambled_symbols.clear();
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publish_frame_info();
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@ -181,20 +170,9 @@ adaptive_dfe_impl::general_work(int noutput_items,
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break;
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} // WAIT_FOR_FRAME_INFO
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case DO_FILTER: {
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// std::cout << "========= offset (DO_FILTER) nitems_read(0)= " << nitems_read(0) << " ==========" << std::endl;
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_rotated_samples.resize(ninput+_nF+1);
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int ninput_processed = 0;
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for (int i=history()-1; i<ninput && nout<noutput_items; i+=_sps, ninput_processed+=_sps) {
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// rotate samples
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if (i == history()-1) {
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_rotator.rotateN(&_rotated_samples[0] + i - _nB,
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in + i - _nB,
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_nB+_nF+1);
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} else {
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_rotator.rotateN(&_rotated_samples[0] + i + _nF+1 - _sps,
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in + i + _nF+1 - _sps,
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_sps);
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}
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for (int i0=history()-1, i=i0; i<ninput && nout<noutput_items; i+=_sps, ninput_processed+=_sps) {
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if (_symbol_counter == _symbols.size()) {
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publish_frame_info();
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publish_soft_dec();
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@ -207,10 +185,29 @@ adaptive_dfe_impl::general_work(int noutput_items,
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_state = WAIT_FOR_FRAME_INFO;
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break;
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}
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// std::cout << "FILTER_CHECK: " << i << " " << i-1-_nB << " " << i+_nF << " " << in[i] << std::endl;
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// rotate samples
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if (i == i0) {
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#if 0
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_rotator.rotateN(&_rotated_samples[0] + i - _nB,
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in + i - _nB,
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_nB+_nF+1);
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#else
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for (int j=0; j<_nB+_nF+1; ++j)
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_rotated_samples[j + i-_nB] = _rotator.rotate(in[j + i-_nB]);
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#endif
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} else {
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#if 0
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_rotator.rotateN(&_rotated_samples[0] + i + _nF+1 - _sps,
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in + i + _nF+1 - _sps,
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_sps);
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#else
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for (int j=0; j<_sps; ++j)
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_rotated_samples[j + i+_nF+1-_sps] = _rotator.rotate(in[j + i+_nF+1-_sps]);
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#endif
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}
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assert(i+_nF < nin && i-1-_nB >= 0);
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out_symb[nout] = filter(&_rotated_samples[0] + i - _nB,
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&_rotated_samples[0] + i + _nF+1);
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out_symb[nout] = filter(&_rotated_samples.front() + i - _nB,
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&_rotated_samples.front() + i + _nF+1);
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std::memcpy(&out_taps[(_nB+_nF+1)*nout], _taps_samples, (_nB+_nF+1)*sizeof(gr_complex));
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++nout;
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} // next sample
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@ -247,8 +244,6 @@ bool adaptive_dfe_impl::stop()
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gr_complex adaptive_dfe_impl::filter(gr_complex const* start, gr_complex const* end) {
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assert(end-start == _nB + _nF + 1);
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_num_samples_since_filter_update += _sps;
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// (1) run the filter filter
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gr_complex filter_output(0);
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// (1a) taps_samples
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@ -259,9 +254,7 @@ gr_complex adaptive_dfe_impl::filter(gr_complex const* start, gr_complex const*
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// (1b) taps_symbols
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gr_complex dot_symbols(0);
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gr::digital::constellation_sptr constell = _constellations[_constellation_index];
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bool const update_taps = constell->bits_per_symbol() <= 3;
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if (constell->bits_per_symbol() > 3)
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_use_symbol_taps = false;
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_use_symbol_taps = (constell->bits_per_symbol() <= 3);
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if (_use_symbol_taps) {
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for (int l=0; l<_nW; ++l) {
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assert(_hist_symbol_index+l < 2*_nW);
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@ -273,6 +266,7 @@ gr_complex adaptive_dfe_impl::filter(gr_complex const* start, gr_complex const*
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gr_complex known_symbol = _symbols[_symbol_counter];
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bool const is_known = std::abs(known_symbol) > 1e-5;
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bool const update_taps = constell->bits_per_symbol() <= 3 || is_known;
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// (2) unknown symbols (=data): compute soft decisions
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if (not is_known) {
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gr_complex const descrambled_filter_output = std::conj(_scramble[_symbol_counter]) * filter_output;
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@ -283,21 +277,18 @@ gr_complex adaptive_dfe_impl::filter(gr_complex const* start, gr_complex const*
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float const err = std::abs(descrambled_filter_output - descrambled_symbol);
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std::vector<float> const soft_dec = constell->calc_soft_dec
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(descrambled_filter_output, _npwr[_constellation_index].filter(err));
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// std::cout << "SD: " << descrambled_filter_output << " : ";
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for (int j=0, m=soft_dec.size(); j<m; ++j) {
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for (int j=0, m=soft_dec.size(); j<m; ++j)
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_vec_soft_decisions.push_back(soft_dec[j] * _scramble_xor[_symbol_counter][j]);
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// std::cout << " " << _vec_soft_decisions.back();
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}
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// std::cout << std::endl;
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}
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known_symbol = _scramble[_symbol_counter] * descrambled_symbol;
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}
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// std::cout << "FILTER: " << filter_output <<" " << known_symbol << " " << start[_nB+1] << std::endl;
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// (3) filter update
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if (is_known || update_taps) {
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if (update_taps) {
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_num_samples_since_filter_update += _sps;
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// (3a) update of adaptive filter taps
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gr_complex const err = filter_output - known_symbol;
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if (std::abs(err)>0.5)
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if (std::abs(err)>0.7)
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std::cout << "err= " << std::abs(err) << std::endl;
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// taps_samples
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for (int j=0; j<_nB+_nF+1; ++j) {
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@ -316,16 +307,23 @@ gr_complex adaptive_dfe_impl::filter(gr_complex const* start, gr_complex const*
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}
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}
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// (3b) control loop update for doppler correction using the adaptibve filter taps
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if (_symbol_counter+1 == _symbols.size()) {
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gr_complex acc(0);
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for (int j=_nB+1-2*_sps; j<_nB+1+2*_sps+1; ++j)
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acc += std::conj(_last_taps_samples[j]) * _taps_samples[j];
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float const frequency_err = gr::fast_atan2f(acc)/(1+0*_num_samples_since_filter_update); // frequency error (rad/sample)
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GR_LOG_DEBUG(d_logger, str(boost::format("frequency_err= %f %d") % frequency_err % _num_samples_since_filter_update));
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_control_loop.advance_loop(frequency_err);
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_control_loop.phase_wrap();
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_control_loop.frequency_limit();
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_rotator.set_phase_incr(gr_expj(_control_loop.get_frequency()));
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if (update_taps) {
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if (_symbol_counter != 0) { // a filter tap shift might have ocurred when _symbol_counter==0
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gr_complex acc(0);
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for (int j=0; j<_nB+_nF+1; ++j) {
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acc += std::conj(_last_taps_samples[j]) * _taps_samples[j];
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}
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float const frequency_err = gr::fast_atan2f(acc)/(0+1*_num_samples_since_filter_update); // frequency error (rad/sample)
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GR_LOG_DEBUG(d_logger, str(boost::format("frequency_err= %f %d") % frequency_err % _num_samples_since_filter_update));
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_control_loop.advance_loop(frequency_err);
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_control_loop.phase_wrap();
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_control_loop.frequency_limit();
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_rotator.set_phase_incr(gr_expj(_control_loop.get_frequency()));
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GR_LOG_DEBUG(d_logger, str(boost::format("frequency_err= %f %d %f")
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% (frequency_err/(2*M_PI)*12000.0)
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% _num_samples_since_filter_update
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% _control_loop.get_frequency()));
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}
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_num_samples_since_filter_update = 0;
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}
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@ -336,29 +334,38 @@ gr_complex adaptive_dfe_impl::filter(gr_complex const* start, gr_complex const*
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int
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adaptive_dfe_impl::recenter_filter_taps() {
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// get max(abs(taps))
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ssize_t const idx_max = std::distance(_taps_samples,
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#if 0
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ssize_t const _idx_max = std::distance(_taps_samples,
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std::max_element(_taps_samples+_nB+1-3*_sps, _taps_samples+_nB+1+3*_sps,
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[](gr_complex a, gr_complex b) {
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return std::norm(a) < std::norm(b);
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}));
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#else
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float sum_w=0, sum_wi=0;
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for (int i=0; i<_nB+_nF+1; ++i) {
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float const w = std::norm(_taps_samples[i]);
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sum_w += w;
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sum_wi += w*i;
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}
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ssize_t const idx_max = ssize_t(0.5 + sum_wi/sum_w);
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#endif
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// GR_LOG_DEBUG(d_logger, str(boost::format("idx_max=%2d abs(tap_max)=%f") % idx_max % std::abs(_taps_samples[idx_max])));
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if (idx_max-_nB-1 > +2*_sps) {
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// maximum is right of the center tap
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// -> shift taps to the left left
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GR_LOG_DEBUG(d_logger, "shift left");
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std::copy(_taps_samples+2*_sps, _taps_samples+_nB+_nF+1, _taps_samples);
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std::fill_n(_taps_samples+_nB+_nF+1-2*_sps, 2*_sps, gr_complex(0));
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return +2*_sps;
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std::copy(_taps_samples+4*_sps, _taps_samples+_nB+_nF+1, _taps_samples);
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std::fill_n(_taps_samples+_nB+_nF+1-4*_sps, 4*_sps, gr_complex(0));
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return +4*_sps;
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}
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if (idx_max-_nB-1 < -2*_sps) {
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// maximum is left of the center tap
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// -> shift taps to the right
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GR_LOG_DEBUG(d_logger, "shift right");
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std::copy_backward(_taps_samples, _taps_samples+_nB+_nF+1-2*_sps,
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std::copy_backward(_taps_samples, _taps_samples+_nB+_nF+1-4*_sps,
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_taps_samples+_nB+_nF+1);
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std::fill_n(_taps_samples, 2*_sps, gr_complex(0));
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return -2*_sps;
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std::fill_n(_taps_samples, 4*_sps, gr_complex(0));
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return -4*_sps;
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}
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return 0;
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}
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@ -378,10 +385,13 @@ void adaptive_dfe_impl::publish_frame_info()
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{
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pmt::pmt_t data = pmt::make_dict();
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GR_LOG_DEBUG(d_logger, str(boost::format("publish_frame_info %d == %d") % _descrambled_symbols.size() % _symbols.size()));
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data = pmt::dict_add(data, pmt::intern("symbols"), pmt::init_c32vector(_descrambled_symbols.size(), &_descrambled_symbols.front()));
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data = pmt::dict_add(data,
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pmt::intern("symbols"),
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pmt::init_c32vector(_descrambled_symbols.size(), &_descrambled_symbols.front()));
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// for (int i=0; i<_vec_soft_decisions.size(); ++i)
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// _vec_soft_decisions[i] = std::max(-1.0f, std::min(1.0f, _vec_soft_decisions[i]));
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data = pmt::dict_add(data, pmt::intern("soft_dec"), pmt::init_f32vector(_vec_soft_decisions.size(), &_vec_soft_decisions.front()));
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data = pmt::dict_add(data,
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pmt::intern("soft_dec"), pmt::init_f32vector(_vec_soft_decisions.size(), &_vec_soft_decisions.front()));
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message_port_pub(_msg_ports["frame_info"], data);
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_descrambled_symbols.clear();
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}
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@ -150,10 +150,21 @@ doppler_correction_cc_impl::work(int noutput_items,
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} // CONSUME_AND_SKIP
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}
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// apply current doppler correction to all produced samples
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#if 0 // rotateN is broken in some older VOLK versions
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_rotator.rotateN(out, in, nout);
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#else
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for (int i=0; i<nout; ++i)
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out[i] = _rotator.rotate(in[i]);
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#endif
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// Tell runtime system how many output items we produced.
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return nout;
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}
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bool
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doppler_correction_cc_impl::stop()
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{
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GR_LOG_DEBUG(d_logger, "doppler_correction stop");
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return true;
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}
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} /* namespace digitalhf */
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} /* namespace gr */
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@ -55,6 +55,7 @@ public:
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gr_vector_const_void_star &input_items,
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gr_vector_void_star &output_items);
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bool stop();
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protected:
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void handle_message(pmt::pmt_t msg);
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@ -33,6 +33,7 @@ class msg_proxy(gr.basic_block):
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in_sig=[],
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out_sig=[])
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self._obj = physical_layer_object
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self._quality = 0.0
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self._port_doppler = pmt.intern("doppler")
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self.message_port_register_in(self._port_doppler)
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@ -47,36 +48,35 @@ class msg_proxy(gr.basic_block):
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self._port_bits = pmt.intern("bits")
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self.message_port_register_out(self._port_bits)
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def start(self):
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return True
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def msg_handler_doppler(self, msg_in):
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## print('-------------------- msg_handler_doppler --------------------')
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iq_samples = pmt.to_python(pmt.cdr(msg_in))
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success,doppler = self._obj.get_doppler(iq_samples)
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msg_out = pmt.make_dict()
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msg_out = pmt.dict_add(msg_out, pmt.intern('success'), pmt.to_pmt(np.bool(success)))
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msg_out = pmt.dict_add(msg_out, pmt.intern('doppler'), pmt.to_pmt(doppler))
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## print(msg_out)
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msg_out = pmt.to_pmt(self._obj.get_doppler(iq_samples))
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self.message_port_pub(self._port_doppler, msg_out)
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def msg_handler_frame(self, msg_in):
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## print('-------------------- msg_handler_frame --------------------')
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## print(msg_in)
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symbols = pmt.to_python(pmt.dict_ref(msg_in, pmt.intern('symbols'), pmt.PMT_NIL))
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soft_dec = pmt.to_python(pmt.dict_ref(msg_in, pmt.intern('soft_dec'), pmt.PMT_NIL))
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symb,constellation_idx,do_continue,save_soft_dec = self._obj.get_next_frame(symbols)
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if do_continue and len(soft_dec) != 0:
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bits = np.array(self._obj.decode_soft_dec(soft_dec), dtype=np.uint8)
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bits,self._quality = self._obj.decode_soft_dec(soft_dec)
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bits = np.array(bits, dtype=np.uint8)
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msg_out = pmt.make_dict()
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msg_out = pmt.dict_add(msg_out, pmt.intern('packet_len'), pmt.to_pmt(len(bits)))
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msg = pmt.cons(msg_out, pmt.to_pmt(bits))
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self.message_port_pub(self._port_bits, msg)
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##print('symb=', symb, symb['symb'], symb['scramble'])
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msg_out = pmt.make_dict()
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msg_out = pmt.dict_add(msg_out, pmt.intern('symb'), pmt.to_pmt(symb['symb']))
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msg_out = pmt.dict_add(msg_out, pmt.intern('scramble'), pmt.to_pmt(symb['scramble']))
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msg_out = pmt.dict_add(msg_out, pmt.intern('scramble_xor'), pmt.to_pmt(symb['scramble_xor']))
|
||||
msg_out = pmt.dict_add(msg_out, pmt.intern('constellation_idx'), pmt.to_pmt(constellation_idx))
|
||||
msg_out = pmt.dict_add(msg_out, pmt.intern('do_continue'), pmt.to_pmt(np.bool(do_continue)))
|
||||
msg_out = pmt.dict_add(msg_out, pmt.intern('save_soft_dec'), pmt.to_pmt(np.bool(save_soft_dec)))
|
||||
## print(msg_out)
|
||||
msg_out = pmt.to_pmt({'symb': symb['symb'],
|
||||
'scramble': symb['scramble'],
|
||||
'scramble_xor': symb['scramble_xor'],
|
||||
'constellation_idx': constellation_idx,
|
||||
'do_continue': np.bool(do_continue),
|
||||
'save_soft_dec': np.bool(save_soft_dec)})
|
||||
self.message_port_pub(self._port_frame_info, msg_out)
|
||||
|
||||
def get_quality(self):
|
||||
return ('%5.1f %%' % self._quality)
|
||||
|
|
|
|||
|
|
@ -266,6 +266,7 @@ class PhysicalLayer(object):
|
|||
self._preamble = self.get_preamble()
|
||||
self._scramble = ScrambleData()
|
||||
self._viterbi_decoder = viterbi27(0x6d, 0x4f)
|
||||
self._mode_description = 'UNKNOWN'
|
||||
|
||||
def get_constellations(self):
|
||||
return self._constellations
|
||||
|
|
@ -307,7 +308,8 @@ class PhysicalLayer(object):
|
|||
|
||||
def get_doppler(self, iq_samples):
|
||||
"""quality check and doppler estimation for preamble"""
|
||||
success,doppler = True,0
|
||||
r = {'success': False, ## -- quality flag
|
||||
'doppler': 0} ## -- doppler estimate (rad/symb)
|
||||
if len(iq_samples) != 0:
|
||||
sps = self._sps
|
||||
m = 23*sps
|
||||
|
|
@ -325,20 +327,23 @@ class PhysicalLayer(object):
|
|||
zp[i*m+idx])[11*sps+np.arange(-2*sps,2*sps)]))
|
||||
for i in range((n//m)-1)]
|
||||
tests = np.abs(pks[0:-1])/val
|
||||
success = np.median(tests) > 2.0
|
||||
r['success'] = bool(np.median(tests) > 2.0)
|
||||
print('test:', np.abs(pks), tests)
|
||||
if success:
|
||||
if r['success']:
|
||||
print('doppler apks', np.abs(pks))
|
||||
print('doppler ppks', np.angle(pks),
|
||||
np.diff(np.unwrap(np.angle(pks)))/m,
|
||||
np.mean(np.diff(np.unwrap(np.angle(pks)))/m))
|
||||
doppler = common.freq_est(pks)/m;
|
||||
print('success=', success, 'doppler=', doppler)
|
||||
return success,doppler
|
||||
r['doppler'] = common.freq_est(pks)/m
|
||||
print(r)
|
||||
return r
|
||||
|
||||
def set_mode(self, mode):
|
||||
pass
|
||||
|
||||
def get_mode(self):
|
||||
return self._mode_description
|
||||
|
||||
def get_preamble_quality(self, symbols):
|
||||
print('get_preamble_quality', np.abs(np.mean(symbols[-32:])), symbols[-32:])
|
||||
return np.abs(np.mean(symbols[-32:])) > 0.5
|
||||
|
|
@ -354,7 +359,6 @@ class PhysicalLayer(object):
|
|||
def is_HFXL(self):
|
||||
return self._mode_name == 'HFXL'
|
||||
|
||||
|
||||
def decode_reinserted_preamble(self, symbols):
|
||||
## decode D0,D1,D2
|
||||
success = True
|
||||
|
|
@ -385,13 +389,14 @@ class PhysicalLayer(object):
|
|||
if rate_info['baud'] == 'HFXL':
|
||||
self._mode_name = 'HFXL'
|
||||
|
||||
self._mode_description = '%s rate=%s intl=%s' % (self._mode_name, rate_info['baud'], intl_info['id'])
|
||||
|
||||
print('======== rate,interleaver:', rate_info, intl_info, self._mode_name)
|
||||
self._data_scramble_xor = np.zeros(256, dtype=np.uint8)
|
||||
self._data_scramble = np.ones (256, dtype=np.complex64)
|
||||
if self.is_12800bpsBurst():
|
||||
self._scrp = ScrambleDataP()
|
||||
self._constellation_index = MODE_BPSK# 64QAMp
|
||||
##self._data_scramble = QAM64p['points'][self._scrp.next() for _ in range(256)]
|
||||
self._constellation_index = MODE_BPSK
|
||||
elif self.is_HFXL():
|
||||
self._scramble.reset()
|
||||
num_bits = 3
|
||||
|
|
@ -511,23 +516,25 @@ class PhysicalLayer(object):
|
|||
soft_bits[2*i] = abs_soft_dec*(2*(b>>1)-1)
|
||||
soft_bits[2*i+1] = abs_soft_dec*(2*(b &1)-1)
|
||||
|
||||
return soft_bits>0
|
||||
return soft_bits>0,100.0
|
||||
elif self.is_HFXL():
|
||||
## TODO
|
||||
return []
|
||||
return np.zeros(0, dtype=np.float32),0.0
|
||||
|
||||
elif self.is_plain_110C():
|
||||
r = self._deintl_depunct.load(soft_dec)
|
||||
if r.shape[0] == 0:
|
||||
return []
|
||||
return np.zeros(0, dtype=np.float32),0.0
|
||||
self._viterbi_decoder.reset()
|
||||
decoded_bits = np.roll(self._viterbi_decoder.udpate(r), 7)
|
||||
print('bits=', decoded_bits[:100])
|
||||
print('quality={}% ({},{})'.format(120.0*self._viterbi_decoder.quality()/(2*len(decoded_bits)),
|
||||
quality = 120.0*self._viterbi_decoder.quality()/(2*len(decoded_bits))
|
||||
print('quality={}% ({},{})'.format(quality,
|
||||
self._viterbi_decoder.quality(),
|
||||
len(decoded_bits)))
|
||||
return decoded_bits
|
||||
return decoded_bits,quality
|
||||
else:
|
||||
return []
|
||||
return np.zeros(0, dtype=np.float32),0.0
|
||||
|
||||
@staticmethod
|
||||
def get_preamble():
|
||||
|
|
|
|||
|
|
@ -43,7 +43,6 @@ class PhysicalLayer(object):
|
|||
def __init__(self, sps):
|
||||
"""intialization"""
|
||||
self._sps = sps
|
||||
##self._mode = self.MODE_QPSK
|
||||
self._frame_counter = 0
|
||||
self._is_first_frame = True
|
||||
self._constellations = [self.make_psk(2, [0,1]),
|
||||
|
|
@ -52,16 +51,20 @@ class PhysicalLayer(object):
|
|||
self._preamble = self.get_preamble()
|
||||
self._data = self.get_data()
|
||||
self._viterbi_decoder = viterbi27(0x6d, 0x4f)
|
||||
self._mode_description = None
|
||||
|
||||
def set_mode(self, mode):
|
||||
"""set phase modultation type: 'BPS/S' or 'BPS/L'"""
|
||||
print('set_mode', mode)
|
||||
"""set modulation and interleaver: 'BPS/S' or 'BPS/L'"""
|
||||
self._mode_description = mode
|
||||
bps,intl = mode.split('/')
|
||||
self._mode = MODES[bps]['const']
|
||||
self._deinterleaver = Deinterleaver(DEINTERLEAVER_INCR[intl] * MODES[bps]['deintl_multiple'])
|
||||
self._depuncturer = common.Depuncturer(repeat = MODES[bps]['repeat'],
|
||||
puncture_pattern = MODES[bps]['punct'])
|
||||
|
||||
def get_mode(self):
|
||||
return self._mode_description
|
||||
|
||||
def get_constellations(self):
|
||||
return self._constellations
|
||||
|
||||
|
|
@ -71,31 +74,26 @@ class PhysicalLayer(object):
|
|||
[1] ... modulation type after descrambling
|
||||
[2] ... a boolean indicating if the processing should continue
|
||||
[3] ... a boolean indicating if the soft decision for the unknown symbols are saved"""
|
||||
## print('-------------------- get_frame --------------------', self._frame_counter, len(symbols))
|
||||
if len(symbols) == 0: ## 1st preamble
|
||||
self._frame_counter = 0
|
||||
|
||||
success,frame_description = True,[]
|
||||
if (self._frame_counter%2) == 0:
|
||||
if (self._frame_counter%2) == 0: ## current frame is a data frame
|
||||
frame_description = [self._preamble,MODE_BPSK,success,False]
|
||||
else:
|
||||
else: ## current frame is a preamble frame
|
||||
idx = range(30,80)
|
||||
z = symbols[idx]*np.conj(self._preamble['symb'][idx])
|
||||
## print('quality_preamble',np.sum(np.real(z)<0), symbols[idx])
|
||||
success = np.sum(np.real(z)<0) < 30
|
||||
success = bool(np.sum(np.real(z)<0) < 30)
|
||||
frame_description = [self._data,self._mode,success,True]
|
||||
|
||||
self._frame_counter += 1
|
||||
return frame_description
|
||||
|
||||
def get_doppler(self, iq_samples):
|
||||
"""returns a tuple
|
||||
[0] ... quality flag
|
||||
[1] ... doppler estimate (rad/symbol) if available"""
|
||||
## print('-------------------- get_doppler --------------------', self._frame_counter,len(iq_samples))
|
||||
success,doppler = False,0
|
||||
r = {'success': False, ## -- quality flag
|
||||
'doppler': 0} ## -- doppler estimate (rad/symb)
|
||||
if len(iq_samples) == 0:
|
||||
return success,doppler
|
||||
return r
|
||||
|
||||
sps = self._sps
|
||||
zp = np.array([x for x in self._preamble['symb'][9:40]
|
||||
|
|
@ -104,20 +102,10 @@ class PhysicalLayer(object):
|
|||
imax = np.argmax(np.abs(cc[0:18*sps]))
|
||||
pks = cc[(imax,imax+31*sps),]
|
||||
tpks = cc[imax+15*sps:imax+16*sps]
|
||||
## print('doppler: ', np.abs(pks), np.abs(tpks))
|
||||
success = np.mean(np.abs(pks)) > 5*np.mean(np.abs(tpks))
|
||||
doppler = np.diff(np.unwrap(np.angle(pks)))[0]/31/self._sps if success else 0
|
||||
return success,doppler
|
||||
|
||||
def is_preamble(self):
|
||||
return self._frame_counter == 0
|
||||
|
||||
def quality_data(self, s):
|
||||
"""quality check for the data frame"""
|
||||
known_symbols = np.mod(range(176),48)>=32
|
||||
print('quality_data',np.sum(np.real(s[known_symbols])<0))
|
||||
success = np.sum(np.real(s[known_symbols])<0) < 20
|
||||
return success,0 ## no doppler estimate for data frames
|
||||
r['success'] = bool(np.mean(np.abs(pks)) > 5*np.mean(np.abs(tpks)))
|
||||
r['doppler'] = np.diff(np.unwrap(np.angle(pks)))[0]/31/self._sps if r['success'] else 0
|
||||
return r
|
||||
|
||||
def get_preamble_z(self):
|
||||
"""preamble symbols for preamble correlation"""
|
||||
|
|
@ -132,9 +120,8 @@ class PhysicalLayer(object):
|
|||
r.extend(self._deinterleaver.fetch().tolist())
|
||||
rd = self._depuncturer.process(np.array(r, dtype=np.float32))
|
||||
decoded_bits = self._viterbi_decoder.udpate(rd)
|
||||
print('bits=', decoded_bits)
|
||||
print('quality={}%'.format(100.0*self._viterbi_decoder.quality()/(2*len(decoded_bits))))
|
||||
return decoded_bits
|
||||
quality = 100.0*self._viterbi_decoder.quality()/(2*len(decoded_bits))
|
||||
return decoded_bits,quality
|
||||
|
||||
@staticmethod
|
||||
def get_preamble():
|
||||
|
|
@ -143,8 +130,8 @@ class PhysicalLayer(object):
|
|||
taps = np.array([0,0,1,0,1], dtype=np.bool)
|
||||
p = np.zeros(80, dtype=np.uint8)
|
||||
for i in range(80):
|
||||
p[i] = state[-1]
|
||||
state = np.concatenate(([np.sum(state&taps)&1], state[0:-1]))
|
||||
p[i] = state[-1]
|
||||
state = np.concatenate(([np.sum(state&taps)&1], state[0:-1]))
|
||||
a = np.zeros(80, common.SYMB_SCRAMBLE_DTYPE)
|
||||
## BPSK modulation
|
||||
constellation = PhysicalLayer.make_psk(2,range(2))['points']
|
||||
|
|
@ -156,7 +143,7 @@ class PhysicalLayer(object):
|
|||
def get_data():
|
||||
"""data symbols + scrambler; for unknown symbols 'symb'=0"""
|
||||
state = np.array([1,1,1,1,1,1,1,1,1], dtype=np.bool)
|
||||
taps = np.array([0,0,0,0,1,0,0,0,1], dtype=np.bool)
|
||||
taps = np.array([0,0,0,0,1,0,0,0,1], dtype=np.bool)
|
||||
p = np.zeros(176, dtype=np.uint8)
|
||||
for i in range(176):
|
||||
p[i] = np.sum(state[-3:]*[4,2,1])
|
||||
|
|
|
|||
|
|
@ -103,3 +103,9 @@ class physical_layer_driver(gr.hier_block2):
|
|||
|
||||
def set_mode(self, mode):
|
||||
self._physical_layer_driver_description.set_mode(mode)
|
||||
|
||||
def get_quality(self):
|
||||
return self._msg_proxy.get_quality()
|
||||
|
||||
def get_mode(self):
|
||||
return self._physical_layer_driver_description.get_mode()
|
||||
|
|
|
|||
Loading…
Reference in a new issue