kopia lustrzana https://github.com/ha7ilm/csdr
110 wiersze
3.4 KiB
Matlab
Executable File
110 wiersze
3.4 KiB
Matlab
Executable File
#!/usr/bin/octave
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%you need to first install the parallel and struct packages:
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%pkg install -forge struct
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%pkg install -forge parallel
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pkg load parallel
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function y=inarg(x)
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for i=1:length(argv())
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if strcmp(argv(){i},x)
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y=1;
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return
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end
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end
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y=0;
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end
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if !inarg('--nogen')
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fwrite(stdout, "===========================================\nGenerating baseband signal from random data\n===========================================\n");
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system('cat /dev/urandom | csdr pack_bits_8to1_u8_u8 | csdr psk_modulator_u8_c 2 | csdr gain_ff 0.25 | csdr psk31_interpolate_sine_cc 256 | csdr add_n_zero_samples_at_beginning_f 170 | pv -ps 2g | dd iflag=fullblock bs=128M count=16 of=/tmp/psk31-raw-data');
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fwrite(stdout, "===========================================\nGenerating Gaussian white noise for agwn_cc\n===========================================\n");
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system('csdr gaussian_noise_c | pv -ps 256m | dd of=/tmp/psk31-gaussian-noise iflag=fullblock bs=256M count=1');
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end
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if inarg('--onlygen')
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exit(0)
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end
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fwrite(stdout, "===========================================\nCalculating variance graph data \n===========================================\n");
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function output=shrun(cmd, type, minsize)
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SIGTERM=15;
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output=[];
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cmd
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[pin, pout, pid]=popen2('bash',{'-c', cmd});
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%fclose(pin);
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do
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sleep(0.3)
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fwrite(stdout,'.');
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%size(output)
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%output
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current_output=fread(pout, Inf, type);
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frewind(pout);
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output=[output; current_output];
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until(size(output)(1)>=minsize)
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waitpid(pid);
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kill(pid, SIGTERM);
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fclose(pin);
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fclose(pout);
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end
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function variance=run_var(snr, which_ted)
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disp('ran a command')
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out_vect=shrun(sprintf('cat /tmp/psk31-raw-data | csdr awgn_cc %d --awgnfile /tmp/psk31-gaussian-noise | csdr timing_recovery_cc %s 256 --add_q --output_indexes | CSDR_FIXED_BUFSIZE=1048576 csdr normalized_timing_variance_u32_f 256 85', snr, which_ted), 'float32', 1);
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disp('run_var output:');
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out_vect'
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variance=out_vect(1);
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end
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function variances=mkvarplot(which_ted, snrs)
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fun = @(x) run_var(x, which_ted);
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variances=pararrayfun(nproc, fun, snrs);
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%{
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variances=[]
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for snr=snrs
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snr
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variances=[variances run_var(snr, which_ted)];
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end
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%}
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end
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function fmtplot(h)
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FN = findall(h,'-property','FontName');
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set(FN,'FontName','/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerifCondensed.ttf');
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set(FN,'FontName','times');
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FS = findall(h,'-property','FontSize');
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set(FS,'FontSize',18);
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xlabel('E_b/N_0 [dB]');
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ylabel('Phase error variance [rad^2]');
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end
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snrs=-5:5:30
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%snrs=[10]
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error_values_gardner=mkvarplot('GARDNER',snrs);
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%{
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snrs_earlylate=0:256
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error_values_earlylate=mkvarplot('EARLYLATE',snrs_earlylate);
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%}
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%graphics_toolkit("gnuplot")
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h=figure(1);
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ebn0=snrs-13.26-10*log10(1/256.)
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%13.56 dB is the difference between the real (measured) SNR and the number input to awgn_cc.
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%This is because agwn_cc assumes a signal with 0dB power at te input, while our BPSK31 baseband signal is of -13.26 dB.
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semilogy(ebn0, error_values_gardner, 'linewidth', 2);
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title('Estimation variance');
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fmtplot(h)
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pause
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%{
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semilogy(snrs_earlylate, error_values_earlylate, 'linewidth', 2);
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title('S-curve for early-late TED');
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fmtplot(h)
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pause
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%}
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if !inarg('--nogen')
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system('rm /tmp/psk31-raw-data /tmp/psk31-gaussian-noise');
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end
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