mirror of
https://github.com/hb9fxq/gr-digitalhf
synced 2024-12-22 15:10:00 +00:00
use more of numpy; improved doppler estimation
This commit is contained in:
cmayer
parent
fba138b288
commit
39a8426822
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@ -191,7 +191,7 @@
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</param>
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<param>
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<key>value</key>
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<value>0.001</value>
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<value>0.005</value>
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</param>
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<param>
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<key>_enabled</key>
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@ -467,6 +467,57 @@
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<value>1</value>
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</param>
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</block>
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<block>
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<key>blocks_file_sink</key>
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<param>
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<key>append</key>
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<value>False</value>
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</param>
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<param>
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<key>alias</key>
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<value></value>
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</param>
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<param>
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<key>comment</key>
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<value></value>
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</param>
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<param>
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<key>affinity</key>
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<value></value>
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</param>
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<param>
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<key>_enabled</key>
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<value>True</value>
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</param>
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<param>
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<key>file</key>
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<value>/Users/chm/Software/gr-digitalhf/examples/soft_dec.bin</value>
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</param>
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<param>
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<key>_coordinate</key>
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<value>(693, 677)</value>
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</param>
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<param>
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<key>_rotation</key>
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<value>0</value>
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</param>
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<param>
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<key>id</key>
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<value>blocks_file_sink_0</value>
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</param>
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<param>
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<key>type</key>
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<value>float</value>
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</param>
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<param>
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<key>unbuffered</key>
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<value>True</value>
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</param>
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<param>
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<key>vlen</key>
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<value>1</value>
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</param>
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</block>
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<block>
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<key>blocks_float_to_complex</key>
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<param>
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@ -561,6 +612,53 @@
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<value>1</value>
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</param>
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</block>
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<block>
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<key>blocks_pdu_to_tagged_stream</key>
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<param>
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<key>alias</key>
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<value></value>
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</param>
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<param>
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<key>comment</key>
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<value></value>
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</param>
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<param>
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<key>affinity</key>
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<value></value>
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</param>
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<param>
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<key>_enabled</key>
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<value>True</value>
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</param>
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<param>
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<key>_coordinate</key>
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<value>(458, 581)</value>
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</param>
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<param>
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<key>_rotation</key>
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<value>0</value>
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</param>
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<param>
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<key>id</key>
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<value>blocks_pdu_to_tagged_stream_0</value>
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</param>
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<param>
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<key>type</key>
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<value>float</value>
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</param>
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<param>
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<key>tag</key>
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<value>packet_len</value>
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</param>
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<param>
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<key>maxoutbuf</key>
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<value>0</value>
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</param>
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<param>
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<key>minoutbuf</key>
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<value>0</value>
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</param>
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</block>
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<block>
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<key>blocks_tag_debug</key>
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<param>
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@ -766,7 +864,7 @@
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</param>
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<param>
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<key>alpha</key>
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<value>0.0005</value>
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<value>0.0001</value>
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</param>
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<param>
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<key>mode</key>
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@ -2022,11 +2120,11 @@
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</param>
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<param>
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<key>size</key>
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<value>1024</value>
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<value>768</value>
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</param>
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<param>
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<key>srate</key>
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<value>samp_rate/sps/2</value>
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<value>samp_rate/sps/3</value>
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</param>
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<param>
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<key>stemplot</key>
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@ -2046,7 +2144,7 @@
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</param>
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<param>
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<key>tr_mode</key>
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<value>qtgui.TRIG_MODE_AUTO</value>
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<value>qtgui.TRIG_MODE_TAG</value>
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</param>
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<param>
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<key>tr_slope</key>
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@ -2054,11 +2152,11 @@
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</param>
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<param>
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<key>tr_tag</key>
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<value>"soft_dec"</value>
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<value>"packet_len"</value>
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</param>
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<param>
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<key>type</key>
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<value>msg_float</value>
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<value>float</value>
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</param>
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<param>
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<key>update_time</key>
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@ -2322,6 +2420,18 @@
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<source_key>0</source_key>
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<sink_key>0</sink_key>
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</connection>
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<connection>
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<source_block_id>blocks_pdu_to_tagged_stream_0</source_block_id>
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<sink_block_id>blocks_file_sink_0</sink_block_id>
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<source_key>0</source_key>
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<sink_key>0</sink_key>
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</connection>
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<connection>
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<source_block_id>blocks_pdu_to_tagged_stream_0</source_block_id>
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<sink_block_id>qtgui_time_sink_x_1</sink_block_id>
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<source_key>0</source_key>
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<sink_key>0</sink_key>
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</connection>
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<connection>
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<source_block_id>blocks_throttle_0</source_block_id>
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<sink_block_id>fir_filter_xxx_0</sink_block_id>
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@ -2348,9 +2458,9 @@
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</connection>
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<connection>
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<source_block_id>digitalhf_adaptive_dfe_0</source_block_id>
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<sink_block_id>qtgui_time_sink_x_1</sink_block_id>
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<sink_block_id>blocks_pdu_to_tagged_stream_0</sink_block_id>
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<source_key>soft_dec</source_key>
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<sink_key>in</sink_key>
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<sink_key>pdus</sink_key>
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</connection>
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<connection>
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<source_block_id>fir_filter_xxx_0</source_block_id>
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@ -93,6 +93,7 @@ adaptive_dfe_impl::adaptive_dfe_impl(int sps, // samples per symbol
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, _nW(nW)
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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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@ -183,6 +184,7 @@ adaptive_dfe_impl::general_work(int noutput_items,
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std::fill_n(_hist_symbols, 2*_nW, gr_complex(0));
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std::fill_n(_taps_samples, _nB+_nF+1, gr_complex(0));
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std::fill_n(_taps_symbols, _nW, gr_complex(0));
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_use_symbol_taps = true;
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_samples.clear();
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_phase = -phase_est;
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_taps_samples[_nB+1] = 0.01;
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@ -279,8 +281,10 @@ adaptive_dfe_impl::general_work(int noutput_items,
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}
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// publish soft decisions
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if (!_vec_soft_decisions.empty()) {
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std::cout << "soft_dec " << _vec_soft_decisions.size() << "\n";
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unsigned int const bits_per_symbol = _constellations[_constellation_index]->bits_per_symbol();
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_msg_metadata = pmt::dict_add(_msg_metadata, pmt::mp("bits_per_symbol"), pmt::from_long(bits_per_symbol));
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_msg_metadata = pmt::dict_add(_msg_metadata, pmt::mp("packet_len"), pmt::mp(_vec_soft_decisions.size()));
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message_port_pub(_msg_port_name,
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pmt::cons(_msg_metadata,
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pmt::init_f32vector(_vec_soft_decisions.size(), _vec_soft_decisions)));
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@ -382,16 +386,21 @@ gr_complex adaptive_dfe_impl::filter() {
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_taps_samples,
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_nB+_nF+1);
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gr_complex dot_symbols=0;
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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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dot_symbols += _hist_symbols[_hist_symbol_index+l]*_taps_symbols[l];
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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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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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dot_symbols += _hist_symbols[_hist_symbol_index+l]*_taps_symbols[l];
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}
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filter_output += dot_symbols;
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}
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filter_output += dot_symbols;
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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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if (not is_known) { // not known
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gr_complex const descrambled_filter_output = std::conj(_scramble[_symbol_counter]) * filter_output;
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gr::digital::constellation_sptr constell = _constellations[_constellation_index];
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unsigned int jc = constell->decision_maker(&descrambled_filter_output);
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gr_complex descrambled_symbol = 0;
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constell->map_to_points(jc, &descrambled_symbol);
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@ -403,29 +412,24 @@ gr_complex adaptive_dfe_impl::filter() {
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_npwr[_constellation_index] = (1-alpha)*_npwr[_constellation_index] + alpha*err;
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std::vector<float> const soft_dec = constell->calc_soft_dec(descrambled_filter_output, _npwr[_constellation_index]);
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std::copy(soft_dec.begin(), soft_dec.end(), std::back_inserter<std::vector<float> >(_vec_soft_decisions));
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// std::cout << "soft_dec " << _npwr[_constellation_index] << " : ";
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// for (int k=0; k<soft_dec.size(); ++k) {
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// std::cout << soft_dec[k] << " ";
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// }
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// std::cout << "\n";
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}
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known_symbol = _scramble[_symbol_counter] * descrambled_symbol;
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}
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gr_complex err = filter_output - known_symbol;
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for (int j=0; j<_nB+_nF+1; ++j) {
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assert(_hist_sample_index+j < 2*(_nB+_nF+1));
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_taps_samples[j] -= _mu*err*std::conj(_hist_samples[_hist_sample_index+j]);
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}
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for (int j=0; j<_nW; ++j) {
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assert(_hist_symbol_index+j < 2*_nW);
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_taps_symbols[j] -= _mu*err*std::conj(_hist_symbols[_hist_symbol_index+j]) + _alpha*_taps_symbols[j];
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}
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// if (_sample_counter < 80*5)
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// std::cout << "filter: " << _symbol_counter << " " << _sample_counter << " " << filter_output << " " << known_symbol << " " << std::abs(err) << std::endl;
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if (is_known || true) {
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_hist_symbols[_hist_symbol_index] = _hist_symbols[_hist_symbol_index + _nW] = known_symbol;
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if (++_hist_symbol_index == _nW)
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_hist_symbol_index = 0;
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if (is_known || update_taps) {
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gr_complex const err = filter_output - known_symbol;
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for (int j=0; j<_nB+_nF+1; ++j) {
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assert(_hist_sample_index+j < 2*(_nB+_nF+1));
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_taps_samples[j] -= _mu*err*std::conj(_hist_samples[_hist_sample_index+j]);
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}
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if (_use_symbol_taps) {
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for (int j=0; j<_nW; ++j) {
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assert(_hist_symbol_index+j < 2*_nW);
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_taps_symbols[j] -= _mu*err*std::conj(_hist_symbols[_hist_symbol_index+j]) + _alpha*_taps_symbols[j];
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}
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_hist_symbols[_hist_symbol_index] = _hist_symbols[_hist_symbol_index + _nW] = known_symbol;
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if (++_hist_symbol_index == _nW)
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_hist_symbol_index = 0;
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}
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}
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_descrambled_symbols[_symbol_counter] = filter_output*std::conj(_scramble[_symbol_counter]);
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return filter_output*std::conj(_scramble[_symbol_counter++]);
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@ -37,6 +37,8 @@ private:
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float _mu;
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float _alpha;
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bool _use_symbol_taps;
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// module name w.r.t. digitalhf.physical_layer containing a PhysicalLayer class
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std::string _py_module_name;
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boost::python::object _physicalLayer; // class instance of physical layer description
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@ -32,6 +32,7 @@ endif()
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GR_PYTHON_INSTALL(
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FILES
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__init__.py
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common.py
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STANAG_4285.py
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MIL_STD_188_110A.py
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MIL_STD_188_110C.py
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@ -2,6 +2,7 @@
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from __future__ import print_function
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import numpy as np
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from common import *
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## ---- Walsh-4 codes -----------------------------------------------------------
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WALSH = np.array([[0,0,0,0, 0,0,0,0],
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@ -14,12 +15,9 @@ WALSH = np.array([[0,0,0,0, 0,0,0,0],
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[0,1,1,0, 1,0,0,1]],
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dtype=np.uint8)
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def walsh_to_num(w):
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return sum(w*(1<<np.arange(8)[::-1]))
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FROM_WALSH = -np.ones(256, dtype=np.int8)
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for i in range(8):
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FROM_WALSH[walsh_to_num(WALSH[i][:])] = i
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FROM_WALSH[np.packbits(WALSH[i][:])[0]] = i
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## ---- tri-bit codes -----------------------------------------------------------
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TRIBIT = np.zeros((8,32), dtype=np.uint8)
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@ -31,9 +29,6 @@ TRIBIT_SCRAMBLE = np.array(
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[7,4,3,0,5,1,5,0,2,2,1,1,5,7,4,3,5,0,2,6,2,1,6,2,0,0,5,0,5,2,6,6],
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dtype=np.uint8)
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def n_psk(n,x):
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return np.complex64(np.exp(2j*np.pi*x/n))
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## ---- preamble symbols ---------------------------------------------------------
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D1=D2=C1=C2=C3=0 ## not known
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PRE_SYMBOLS = n_psk(2, np.concatenate(
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@ -41,7 +36,7 @@ PRE_SYMBOLS = n_psk(2, np.concatenate(
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PRE_SYMBOLS[9*32:14*32] = 0
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## ---- preamble scramble symbols ------------------------------------------------
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PRE_SCRAMBLE = n_psk(8, np.concatenate([TRIBIT_SCRAMBLE for i in range(15)]))
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PRE_SCRAMBLE = n_psk(8, np.concatenate([TRIBIT_SCRAMBLE for _ in range(15)]))
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## ---- data scrambler -----------------------------------------------------------
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class ScrambleData(object):
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@ -50,13 +45,13 @@ class ScrambleData(object):
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self.reset()
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def reset(self):
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self._state = 0xBAD
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self._state = 0xBAD
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self._counter = 0
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def next(self):
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if self._counter == 160:
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self.reset()
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for j in range(8):
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for _ in range(8):
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self._advance()
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self._counter += 1
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return self._state&7
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@ -68,13 +63,18 @@ class ScrambleData(object):
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self._state ^= 0x053
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return self._state
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## ---- constellatios -----------------------------------------------------------
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BPSK=np.array(zip(np.exp(2j*np.pi*np.arange(2)/2), [0,1]), CONST_DTYPE)
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QPSK=np.array(zip(np.exp(2j*np.pi*np.arange(4)/4), [0,1,3,2]), CONST_DTYPE)
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PSK8=np.array(zip(np.exp(2j*np.pi*np.arange(8)/8), [0,1,3,2,7,6,4,5]), CONST_DTYPE)
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## ---- constellation indices ---------------------------------------------------
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MODE_BPSK=0
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MODE_QPSK=1
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MODE_8PSK=2
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## ---- mode definitions --------------------------------------------------------
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MODE = [[{} for x in range(8)] for y in range(8)]
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MODE = [[{} for _ in range(8)] for _ in range(8)]
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MODE[7][6] = {'bit_rate':4800, 'ci':MODE_8PSK, 'interleaver':['N', 1, 1], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate': -1 }
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MODE[7][7] = {'bit_rate':2400, 'ci':MODE_8PSK, 'interleaver':['N', 1, 1], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate':1./2}
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@ -104,15 +104,12 @@ class PhysicalLayer(object):
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"""intialization"""
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self._sps = sps
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self._frame_counter = -1
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self._constellations = [self.make_psk(2, [0,1]),
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self.make_psk(4, [0,1,3,2]),
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self.make_psk(8, [0,1,3,2,7,6,4,5])] ## TODO: check 8PSK gray code
|
||||
self._constellations = [BPSK, QPSK, PSK8]
|
||||
self._preamble = self.get_preamble()
|
||||
self._pre_counter = -1
|
||||
self._d1d2 = [-1,-1] ## D1,D2
|
||||
self._mode = {}
|
||||
self._scr_data = ScrambleData()
|
||||
##self._data = self.get_data()
|
||||
|
||||
def get_constellations(self):
|
||||
return self._constellations
|
||||
|
|
@ -133,18 +130,19 @@ class PhysicalLayer(object):
|
|||
## ----- data frame ------
|
||||
if self._frame_counter == self._num_frames_per_block:
|
||||
self._frame_counter = 0
|
||||
a = np.zeros(self._frame_len, dtype=[('symb', np.complex64),
|
||||
('scramble', np.complex64)])
|
||||
scramble_for_frame = n_psk(8, np.array([self._scr_data.next()
|
||||
for _ in range(self._frame_len)]))
|
||||
a = np.array(zip(scramble_for_frame,
|
||||
scramble_for_frame),
|
||||
dtype=[('symb', np.complex64),
|
||||
('scramble', np.complex64)])
|
||||
n_unknown = self._mode['unknown']
|
||||
a['symb'] = 1;
|
||||
a['symb'][0:n_unknown] = 0
|
||||
if self._frame_counter >= self._num_frames_per_block-2:
|
||||
idx_d1d2 = self._frame_counter - self._num_frames_per_block + 2;
|
||||
a['symb'][n_unknown :n_unknown+ 8] *= n_psk(2, WALSH[self._d1d2[idx_d1d2]][:])
|
||||
a['symb'][n_unknown+8:n_unknown+16] *= n_psk(2, WALSH[self._d1d2[idx_d1d2]][:])
|
||||
|
||||
a['scramble'] = n_psk(8, np.array([self._scr_data.next() for _ in range(self._frame_len)]))
|
||||
a['symb'] *= a['scramble']
|
||||
self._frame_counter += 1
|
||||
return [a, self._mode['ci'],False,True]
|
||||
|
||||
|
|
@ -154,8 +152,7 @@ class PhysicalLayer(object):
|
|||
[1] ... doppler estimate (rad/symbol) if available"""
|
||||
print('-------------------- get_doppler --------------------',
|
||||
self._frame_counter,len(symbols),len(iq_samples))
|
||||
success = False
|
||||
doppler = 0
|
||||
success,doppler = False,0
|
||||
if self._frame_counter == -1: ## -- preamble ----
|
||||
success,doppler = self.get_doppler_from_preamble(symbols, iq_samples)
|
||||
if len(symbols) != 0:
|
||||
|
|
@ -174,35 +171,37 @@ class PhysicalLayer(object):
|
|||
|
||||
def get_doppler_from_preamble(self, symbols, iq_samples):
|
||||
"""quality check and doppler estimation for preamble"""
|
||||
success = True
|
||||
doppler = 0
|
||||
success,doppler = True,0
|
||||
if len(iq_samples) != 0:
|
||||
zp = np.conj(self.get_preamble_z(self._sps))[9*self._sps:]
|
||||
cc = np.array([np.sum(iq_samples[i*self._sps:(3*32+i-9)*self._sps]*zp)
|
||||
for i in range(4*32)])
|
||||
acc = np.abs(cc)
|
||||
for i in range(0,len(cc),32):
|
||||
print('i=%3d: '%i,end='')
|
||||
for j in range(32):
|
||||
print('%3.0f ' % acc[i+j], end='')
|
||||
print()
|
||||
imax = np.argmax(np.abs(cc[0:2*32]))
|
||||
pks = cc[(imax,imax+3*16,imax+3*16+1,imax+3*32),]
|
||||
apks = np.abs(pks)
|
||||
print('imax=', imax, 'apks=',apks)
|
||||
success = np.mean(apks[(0,3),]) > 2*np.mean(apks[(1,2),])
|
||||
doppler = np.diff(np.unwrap(np.angle(pks[(0,3),])))[0]/(3*32) if success else 0
|
||||
sps = self._sps
|
||||
zp = np.array([z for z in PhysicalLayer.get_preamble()['symb']
|
||||
for _ in range(sps)], dtype=np.complex64)
|
||||
## find starting point
|
||||
cc = np.correlate(iq_samples, zp[0:3*32*sps])
|
||||
imax = np.argmax(np.abs(cc[0:2*32*sps]))
|
||||
pks = cc[(imax, imax+3*32*sps),]
|
||||
tpks = cc[imax+3*16*sps:imax+5*16*sps]
|
||||
print('imax=', imax, 'apks=',np.abs(pks),
|
||||
np.mean(np.abs(pks)), np.mean(np.abs(tpks)), np.abs(tpks))
|
||||
success = np.mean(np.abs(pks)) > 2*np.mean(np.abs(tpks))
|
||||
doppler = np.diff(np.unwrap(np.angle(pks)))[0]/(3*32) if success else 0
|
||||
if success:
|
||||
idx = np.arange(32*sps)
|
||||
pks = [np.correlate(iq_samples[imax+i*32*sps+idx],
|
||||
zp[i*32*sps+idx])[0]
|
||||
for i in range(9)]
|
||||
doppler = freq_est(pks)/32
|
||||
print('success=', success, 'doppler=', doppler)
|
||||
return success,doppler
|
||||
|
||||
def decode_preamble(self, symbols):
|
||||
data = [FROM_WALSH[walsh_to_num
|
||||
data = [FROM_WALSH[np.packbits
|
||||
(np.real
|
||||
(np.sum
|
||||
(symbols[i:i+32].reshape((4,8)),0))<0)]
|
||||
(symbols[i:i+32].reshape((4,8)),0))<0)[0]]
|
||||
for i in range(0,15*32,32)]
|
||||
print('data=',data)
|
||||
self._pre_counter = sum((np.array(data[11:14])&3)*(1<<2*np.arange(3)[::-1]))
|
||||
self._pre_counter = sum([(x&3)*(1<<2*y) for (x,y) in zip(data[11:14][::-1], range(3))])
|
||||
self._d1d2 = data[9:11]
|
||||
self._mode = MODE[data[9]][data[10]]
|
||||
self._block_len = 11520 if self._mode['interleaver'][0] == 'L' else 1440
|
||||
|
|
@ -213,27 +212,17 @@ class PhysicalLayer(object):
|
|||
@staticmethod
|
||||
def get_preamble():
|
||||
"""preamble symbols + scrambler"""
|
||||
a=np.zeros(15*32, dtype=[('symb', np.complex64),
|
||||
('scramble', np.complex64)])
|
||||
a['symb'] = PRE_SCRAMBLE*PRE_SYMBOLS
|
||||
a['scramble'] = PRE_SCRAMBLE
|
||||
return a
|
||||
return np.array(zip(PRE_SCRAMBLE*PRE_SYMBOLS,
|
||||
PRE_SCRAMBLE),
|
||||
dtype=[('symb', np.complex64),
|
||||
('scramble', np.complex64)])
|
||||
|
||||
@staticmethod
|
||||
def get_preamble_z(sps):
|
||||
"""preamble symbols for preamble correlation"""
|
||||
a = PhysicalLayer.get_preamble()
|
||||
return np.array([z for z in a['symb'][0:32*3]
|
||||
for i in range(sps)])
|
||||
|
||||
@staticmethod
|
||||
def make_psk(n, gray_code):
|
||||
"""generates n-PSK constellation data"""
|
||||
c = np.zeros(n, dtype=[('points', np.complex64),
|
||||
('symbols', np.uint8)])
|
||||
c['points'] = n_psk(n,np.arange(n))
|
||||
c['symbols'] = gray_code
|
||||
return c
|
||||
for _ in range(sps)])
|
||||
|
||||
if __name__ == '__main__':
|
||||
def gen_data_scramble():
|
||||
|
|
@ -245,13 +234,13 @@ if __name__ == '__main__':
|
|||
a = np.zeros(160, dtype=np.uint8)
|
||||
s = 0xBAD
|
||||
for i in range(160):
|
||||
for j in range(8): s = advance(s)
|
||||
for _ in range(8): s = advance(s)
|
||||
a[i] = s&7;
|
||||
return a
|
||||
|
||||
p=PhysicalLayer(5)
|
||||
z1=np.array([x for x in PRE_SYMBOLS for i in range(5)])
|
||||
z2=np.array([x for x in PRE_SCRAMBLE for i in range(5)])
|
||||
z1=np.array([x for x in PRE_SYMBOLS for _ in range(5)])
|
||||
z2=np.array([x for x in PRE_SCRAMBLE for _ in range(5)])
|
||||
z=z1*z2
|
||||
|
||||
for i in range(3):
|
||||
|
|
@ -264,6 +253,5 @@ if __name__ == '__main__':
|
|||
print(gen_data_scramble())
|
||||
|
||||
s=ScrambleData()
|
||||
print(type(s))
|
||||
print([s.next() for _ in range(160)])
|
||||
print([s.next() for _ in range(160)])
|
||||
|
|
|
|||
|
|
@ -2,13 +2,9 @@
|
|||
|
||||
from __future__ import print_function
|
||||
import numpy as np
|
||||
|
||||
def n_psk(n,x):
|
||||
return np.complex64(np.exp(2j*np.pi*x/n))
|
||||
from common import *
|
||||
|
||||
## ---- constellations -----------------------------------------------------------
|
||||
CONST_DTYPE=np.dtype([('points', np.complex64),
|
||||
('symbols', np.uint8)])
|
||||
BPSK=np.array(zip(np.exp(2j*np.pi*np.arange(2)/2), [0,1]), CONST_DTYPE)
|
||||
QPSK=np.array(zip(np.exp(2j*np.pi*np.arange(4)/4), [0,1,3,2]), CONST_DTYPE)
|
||||
PSK8=np.array(zip(np.exp(2j*np.pi*np.arange(8)/8), [0,1,3,2,7,6,4,5]), CONST_DTYPE)
|
||||
|
|
@ -47,6 +43,10 @@ QAM64=np.array(
|
|||
-0.360142-0.932897j, -0.353057-0.588429j, -0.353057-0.117686j, -0.353057-0.353057j],
|
||||
range(64)), CONST_DTYPE)
|
||||
|
||||
## for test
|
||||
QAM64 = QAM64[(7,3,24,56,35,39,60,28),]
|
||||
QAM64['symbols'] = [1, 0, 2, 6, 4, 5, 7, 3]
|
||||
|
||||
## ---- constellation indices ---------------------------------------------------
|
||||
MODE_BPSK = 0
|
||||
MODE_QPSK = 1
|
||||
|
|
@ -62,20 +62,18 @@ class ScrambleData(object):
|
|||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self._state = 1
|
||||
self._state = np.array([0,0,0,0,0,0,0,0,1], dtype=np.bool)
|
||||
self._taps = np.array([0,0,0,0,1,0,0,0,1], dtype=np.bool)
|
||||
|
||||
def next(self, num_bits):
|
||||
r = self._state & ((1<<num_bits)-1)
|
||||
for i in range(num_bits):
|
||||
r = np.packbits(self._state[1:])[0]&((1<<num_bits)-1)
|
||||
for _ in range(num_bits):
|
||||
self._advance()
|
||||
return r
|
||||
|
||||
def _advance(self):
|
||||
lsb = self._state&1
|
||||
self._state = (self._state>>1)&511
|
||||
if lsb:
|
||||
self._state ^= 0x10B
|
||||
return self._state
|
||||
self._state = np.concatenate(([np.sum(self._state&self._taps)&1],
|
||||
self._state[0:-1]))
|
||||
|
||||
## ---- preamble definitions ---------------------------------------------------
|
||||
## 184 = 8*23
|
||||
|
|
@ -132,10 +130,13 @@ class PhysicalLayer(object):
|
|||
## --- preamble frame ----
|
||||
if self._frame_counter == -1:
|
||||
return [self._preamble,MODE_BPSK,True,False]
|
||||
## ----- data frame ------
|
||||
## ----- (re)inserted preamble ------
|
||||
if self._frame_counter == 0:
|
||||
self.a = self.make_reinserted_preamble()
|
||||
return [self.a, MODE_QPSK,False,True]
|
||||
return [self.a, MODE_QPSK,False,False]
|
||||
if self._frame_counter >= 1:
|
||||
self.a = self.make_data_frame()
|
||||
return [self.a, MODE_64QAM,False,True]
|
||||
|
||||
def get_doppler(self, symbols, iq_samples):
|
||||
"""returns a tuple
|
||||
|
|
@ -145,50 +146,45 @@ class PhysicalLayer(object):
|
|||
self._frame_counter,len(symbols),len(iq_samples))
|
||||
#if len(symbols)!=0:
|
||||
# print('symb=', symbols)
|
||||
success = False
|
||||
doppler = 0
|
||||
success,doppler = False,0
|
||||
if self._frame_counter == -1: ## -- preamble ----
|
||||
success,doppler = self.get_doppler_from_preamble(symbols, iq_samples)
|
||||
if len(symbols) != 0:
|
||||
for s in symbols:
|
||||
print(s)
|
||||
self._frame_counter = 0
|
||||
elif self._frame_counter >= 0 and self._frame_counter <5: ## -- reinserted preamble ----
|
||||
for s in symbols:
|
||||
print(s)
|
||||
self._frame_counter += 1
|
||||
success = True
|
||||
else: ## ------------------------ data frame ----
|
||||
if len(symbols) != 0:
|
||||
for s in symbols:
|
||||
print(s)
|
||||
for s in symbols:
|
||||
print(s)
|
||||
success = False
|
||||
self._frame_counter = -1
|
||||
return success,doppler
|
||||
|
||||
def get_doppler_from_preamble(self, symbols, iq_samples):
|
||||
"""quality check and doppler estimation for preamble"""
|
||||
success = True
|
||||
doppler = 0
|
||||
shift=9
|
||||
success,doppler = True,0
|
||||
if len(iq_samples) != 0:
|
||||
zp = np.conj(self.get_preamble_z(self._sps)[shift*self._sps:])
|
||||
cc = np.array([np.sum(iq_samples[i:i+23*self._sps] *
|
||||
zp[0:23*self._sps])
|
||||
for i in range(23*3*self._sps)])
|
||||
acc = np.abs(cc)
|
||||
for i in range(0,len(cc),23*self._sps):
|
||||
print('i=%3d: '%i,end='')
|
||||
for j in range(23*self._sps):
|
||||
print('%3.0f ' % acc[i+j], end='')
|
||||
print()
|
||||
|
||||
imax = np.argmax(np.abs(cc[0:3*23*self._sps]))
|
||||
print(imax)
|
||||
pks = np.array([np.sum(iq_samples[(imax+23*i*self._sps):
|
||||
(imax+23*i*self._sps+23*self._sps)] *
|
||||
zp[(23*i*self._sps):
|
||||
(23*i*self._sps+23*self._sps)])
|
||||
for i in range(1,5)])
|
||||
print('doppler apks', np.abs(pks))
|
||||
print('doppler ppks', np.angle(pks), np.diff(np.unwrap(np.angle(pks)))/23)
|
||||
doppler = np.mean(np.diff(np.unwrap(np.angle(pks))))/23
|
||||
success = True
|
||||
sps = self._sps
|
||||
idx = np.arange(23*sps)
|
||||
zp = self.get_preamble_z(self._sps)
|
||||
cc = np.correlate(iq_samples, zp[idx])
|
||||
imax = np.argmax(np.abs(cc[0:23*sps]))
|
||||
pks = [np.correlate(iq_samples[imax+i*23*sps+idx],
|
||||
zp[i*23*sps+idx])[0]
|
||||
for i in range(7)]
|
||||
success = np.mean(np.abs(pks)) > 2*np.mean(np.abs(cc[imax+11*sps+range(-sps,sps)]))
|
||||
print('test:',imax, np.mean(np.abs(pks)), np.mean(np.abs(cc[imax+11*sps+range(-sps,sps)])))
|
||||
if success:
|
||||
print('doppler apks', np.abs(pks))
|
||||
print('doppler ppks', np.angle(pks),
|
||||
np.diff(np.unwrap(np.angle(pks)))/23,
|
||||
np.mean(np.diff(np.unwrap(np.angle(pks)))/23))
|
||||
doppler = freq_est(pks[1:])/23;
|
||||
print('success=', success, 'doppler=', doppler)
|
||||
return success,doppler
|
||||
|
||||
|
|
@ -199,6 +195,13 @@ class PhysicalLayer(object):
|
|||
('scramble', np.complex64)])
|
||||
a['symb'][32:32+3*13] = 0 ## D0,D1,D2
|
||||
return a
|
||||
def make_data_frame(self):
|
||||
a=np.zeros(256+31, dtype=[('symb', np.complex64),
|
||||
('scramble', np.complex64)])
|
||||
a['scramble'] = 1
|
||||
a['symb'][256:] = MINI_PROBE_MINUS
|
||||
a['scramble'][256:] = MINI_PROBE_MINUS
|
||||
return a
|
||||
|
||||
@staticmethod
|
||||
def get_preamble():
|
||||
|
|
@ -210,7 +213,7 @@ class PhysicalLayer(object):
|
|||
@staticmethod
|
||||
def get_preamble_z(sps):
|
||||
"""preamble symbols for preamble correlation"""
|
||||
return np.array([z for z in PREAMBLE for i in range(sps)])
|
||||
return np.array([z for z in PREAMBLE for _ in range(sps)])
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(PREAMBLE)
|
||||
|
|
@ -224,12 +227,16 @@ if __name__ == '__main__':
|
|||
print([s.next(1) for _ in range(511)])
|
||||
print([s.next(1) for _ in range(511)] ==
|
||||
[s.next(1) for _ in range(511)])
|
||||
print(QAM64)
|
||||
print(QAM32)
|
||||
print(QAM16)
|
||||
print(PSK8)
|
||||
print(QPSK)
|
||||
print(BPSK)
|
||||
print(MINI_PROBE_PLUS)
|
||||
print(MINI_PROBE_MINUS)
|
||||
print(MINI_PROBE_PLUS*MINI_PROBE_MINUS)
|
||||
#print(QAM64)
|
||||
#print(QAM32)
|
||||
#print(QAM16)
|
||||
#print(PSK8)
|
||||
#print(QPSK)
|
||||
#print(BPSK)
|
||||
#print(MINI_PROBE_PLUS)
|
||||
#print(MINI_PROBE_MINUS)
|
||||
#print(MINI_PROBE_PLUS*MINI_PROBE_MINUS)
|
||||
#for i in range(len(QAM64)):
|
||||
# print(QAM64['points'][i])
|
||||
|
||||
print([s.next(6) for _ in range(256)])
|
||||
|
|
|
|||
|
|
@ -57,13 +57,15 @@ class PhysicalLayer(object):
|
|||
success = True
|
||||
doppler = 0
|
||||
if len(iq_samples) != 0:
|
||||
zp = [x for x in self._preamble['symb'][9:40] for i in range(self._sps)]
|
||||
cc = np.array([np.sum(iq_samples[ i*5:(31+i)*5]*zp) for i in range(49)])
|
||||
imax = np.argmax(np.abs(cc[0:18]))
|
||||
pks = cc[(imax,imax+15,imax+16,imax+31),]
|
||||
apks = np.abs(pks)
|
||||
success = np.mean(apks[(0,3),]) > 2*np.mean(apks[(1,2),])
|
||||
doppler = np.diff(np.unwrap(np.angle(pks[(0,3),])))[0]/31 if success else 0
|
||||
sps = self._sps
|
||||
zp = np.array([x for x in self._preamble['symb'][9:40]
|
||||
for _ in range(sps)], dtype=np.complex64)
|
||||
cc = np.correlate(iq_samples, zp)
|
||||
imax = np.argmax(np.abs(cc[0:18*sps]))
|
||||
pks = cc[(imax,imax+31*sps),]
|
||||
tpks = cc[imax+15*sps:imax+16*sps]
|
||||
success = np.mean(np.abs(pks)) > 2*np.mean(np.abs(tpks))
|
||||
doppler = np.diff(np.unwrap(np.angle(pks)))[0]/31 if success else 0
|
||||
if len(symbols) != 0:
|
||||
idx = range(30,80) if self._is_first_frame else range(80)
|
||||
z = symbols[idx]*np.conj(self._preamble['symb'][idx])
|
||||
|
|
@ -102,7 +104,7 @@ class PhysicalLayer(object):
|
|||
p = np.zeros(176, dtype=np.uint8)
|
||||
for i in range(176):
|
||||
p[i] = np.sum(state[-3:]*[4,2,1])
|
||||
for j in range(3):
|
||||
for _ in range(3):
|
||||
state = np.concatenate(([np.sum(state&taps)&1], state[0:-1]))
|
||||
a=np.zeros(176, dtype=[('symb',np.complex64), ('scramble', np.complex64)])
|
||||
## 8PSK modulation
|
||||
|
|
@ -116,24 +118,6 @@ class PhysicalLayer(object):
|
|||
def make_psk(n, gray_code):
|
||||
"""generates n-PSK constellation data"""
|
||||
c = np.zeros(n, dtype=[('points', np.complex64), ('symbols', np.uint8)])
|
||||
c['points'] = np.exp(2*np.pi*1j*np.array(range(n))/n)
|
||||
c['points'] = np.exp(2*np.pi*1j*np.arange(n)/n)
|
||||
c['symbols'] = gray_code
|
||||
return c
|
||||
|
||||
## for now not used (doppler estimation after adaptive filtering does not work)
|
||||
@staticmethod
|
||||
def data_aided_frequency_estimation(x,c):
|
||||
"""Data-Aided Frequency Estimation for Burst Digital Transmission,
|
||||
Umberto Mengali and M. Morelli, IEEE TRANSACTIONS ON COMMUNICATIONS,
|
||||
VOL. 45, NO. 1, JANUARY 1997"""
|
||||
z = x*np.conj(c) ## eq (2)
|
||||
L0 = len(z)
|
||||
N = L0//2
|
||||
R = np.zeros(N, dtype=np.complex64)
|
||||
for i in range(N):
|
||||
R[i] = 1.0/(L0-i)*np.sum(z[i:]*np.conj(z[0:L0-i])) ## eq (3)
|
||||
m = np.array(range(N), dtype=np.float)
|
||||
w = 3*((L0-m)*(L0-m+1)-N*(L0-N))/(N*(4*N*N - 6*N*L0 + 3*L0*L0-1)) ## eq (9)
|
||||
mod_2pi = lambda x : np.mod(x-np.pi, 2*np.pi) - np.pi
|
||||
fd = np.sum(w[1:] * mod_2pi(np.diff(np.angle(R)))) ## eq (8)
|
||||
return fd
|
||||
|
|
|
|||
29
python/physical_layer/common.py
Normal file
29
python/physical_layer/common.py
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
## -*- python -*-
|
||||
|
||||
import numpy as np
|
||||
|
||||
CONST_DTYPE=np.dtype([('points', np.complex64),
|
||||
('symbols', np.uint8)])
|
||||
|
||||
def n_psk(n,x):
|
||||
"""n-ary PSK constellation"""
|
||||
return np.complex64(np.exp(2j*np.pi*x/n))
|
||||
|
||||
def freq_est(z):
|
||||
"""Data-Aided Frequency Estimation for Burst Digital Transmission,
|
||||
Umberto Mengali and M. Morelli, IEEE TRANSACTIONS ON COMMUNICATIONS,
|
||||
VOL. 45, NO. 1, JANUARY 1997"""
|
||||
L0 = len(z)
|
||||
N = L0//2
|
||||
R = np.zeros(N+1, dtype=np.complex64)
|
||||
for i in range(N+1):
|
||||
R[i] = 1.0/(L0-i)*np.sum(z[i:]*np.conj(z[0:L0-i])) ## eq (3)
|
||||
m = np.arange(N+1, dtype=np.float32)
|
||||
w = 3*((L0-m)*(L0-m+1)-N*(L0-N))/(N*(4*N*N - 6*N*L0 + 3*L0*L0-1)) ## eq (9)
|
||||
mod_2pi = lambda x : np.mod(x-np.pi, 2*np.pi) - np.pi
|
||||
return np.sum(w[1:] * mod_2pi(np.diff(np.angle(R)))) ## eq (8)
|
||||
|
||||
if __name__ == '__main__':
|
||||
idx=np.arange(3)
|
||||
z=np.exp(1j*idx*0.056+1j)
|
||||
print(freq_est(z)/0.056)
|
||||
Loading…
Reference in a new issue