UWB多径信道模型_通信

% plot_UWB_channel.m clear, clf no_output_files = 0; % non-zero: avoids writing output files of continuous-time responses Ts = 0.167; % sampling time (nsec) num_ch=100; % number of channel impulse responses to generate randn('state',12); % initialize state of function for repeatability rand('state',12); % initialize state of function for repeatability cm = 1; % channel model number from 1 to 4 % get channel model params based on this channel model number [Lam,lam,Gam,gam,nlos,sdi,sdc,sdr] = UWB_parameters(cm); fprintf(1,['Model Parameters\n' ' Lam= %.4f, lam= %.4f, Gam= %.4f, gam= %.4f\n NLOS flag= %d, std_shdw= %.4f, std_ln_1= %.4f, std_ln_2= %.4f\n'],... Lam,lam,Gam,gam,nlos,sdi,sdc,sdr); % get a bunch of realizations (impulse responses) [h_ct,t_ct,t0,np] = UWB_model_ct(Lam,lam,Gam,gam,num_ch,nlos,sdi,sdc,sdr); % now reduce continuous-time result to a discrete-time result [hN,N] = convert_UWB_ct(h_ct,t_ct,np,num_ch,Ts); % if we wanted complex baseband model or to impose some filtering function, % this would be a good place to do it h = resample(hN,1,N); % decimate the columns of hN by factor N h = h*N; % correct for 1/N scaling imposed by decimation channel_energy = sum(abs(h).^2); % channel energy h_len = size(h,1); t = [0:(h_len-1)]*Ts; % for use in computing excess & RMS delays for k=1:num_ch % determine excess delay and RMS delay sq_h = abs(h(:,k)).^2/channel_energy(k); t_norm = t - t0(k); % remove the randomized arrival time of first cluster excess_delay(k) = t_norm*sq_h; rms_delay(k) = sqrt((t_norm-excess_delay(k)).^2*sq_h); % determine # of significant paths (paths within 10 dB from peak) threshold_dB = -10; % dB temp_h = abs(h(:,k)); temp_thresh = 10^(threshold_dB/20)*max(temp_h); num_sig_paths(k) = sum(temp_h>temp_thresh); % determine number of sig. paths (captures x % of energy in channel) x = 0.85; temp_sort = sort(temp_h.^2); % sorted in ascending order of energy cum_energy = cumsum(temp_sort(end:-1:1)); % cumulative energy index_e = min(find(cum_energy >= x*cum_energy(end))); num_sig_e_paths(k) = index_e; end energy_mean = mean(10*log10(channel_energy)); energy_stddev = std(10*log10(channel_energy)); mean_excess_delay = mean(excess_delay); mean_rms_delay = mean(rms_delay); mean_sig_paths = mean(num_sig_paths); mean_sig_e_paths = mean(num_sig_e_paths); fprintf(1,'Model Characteristics\n'); fprintf(1,' Mean delays: excess (tau_m) = %.1f ns, RMS (tau_rms) = %1.f\n', ... mean_excess_delay, mean_rms_delay); fprintf(1,' # paths: NP_10dB = %.1f, NP_85%% = %.1f\n', ... mean_sig_paths, mean_sig_e_paths); fprintf(1,' Channel energy: mean = %.1f dB, std deviation = %.1f dB\n', ... energy_mean, energy_stddev); subplot(421), plot(t,h), grid on title('Impulse response realizations'), xlabel('Time (nS)') subplot(422), plot([1:num_ch], excess_delay, 'b-', ... [1 num_ch], mean_excess_delay*[1 1], 'r--' ); grid on, title('Excess delay (nS)'), xlabel('Channel number') subplot(423), plot([1:num_ch], rms_delay, 'b-', ... [1 num_ch], mean_rms_delay*[1 1], 'r--' ); grid on, title('RMS delay (nS)'), xlabel('Channel number') subplot(424), plot([1:num_ch], num_sig_paths, 'b-', ... [1 num_ch], mean_sig_paths*[1 1], 'r--'); grid on, title('Number of significant paths within 10 dB of peak') xlabel('Channel number') subplot(427), plot([1:num_ch], num_sig_e_paths, 'b-', ... [1 num_ch], mean_sig_e_paths*[1 1], 'r--'); grid on, title('Number of significant paths capturing > 85% energy') xlabel('Channel number') temp_average_power = sum(h'.*(h)')/num_ch; temp_average_power = temp_average_power/max(temp_average_power); average_decay_profile_dB = 10*log10(temp_average_power); subplot(425), plot(t,average_decay_profile_dB); grid on axis([0 t(end) -60 0]), title('Average Power Decay Profile') xlabel('Delay (nsec)'), ylabel('Average power (dB)') subplot(426) figh = plot([1:num_ch],10*log10(channel_energy),'b-', ... [1 num_ch], energy_mean*[1 1], 'g--', ... [1 num_ch], energy_mean+energy_stddev*[1 1], 'r:', ... [1 num_ch], energy_mean-energy_stddev*[1 1], 'r:'); xlabel('Channel number'), ylabel('dB'), title('Channel Energy'); legend(figh, 'Per-channel energy', 'Mean', '\pm Std. deviation', 0) if no_output_files, return; end %%% save continuous-time (time,value) pairs to files save_fn = sprintf('cm%d_imr', cm); % A complete self-contained file for Matlab users save([save_fn '.mat'], 't_ct', 'h_ct', 't0', 'np', 'num_ch', 'cm'); % Two comma-delimited text files for non-Matlab users: % File #1: cmX_imr_np.csv lisTs the number of paths in each realization dlmwrite([save_fn '_np.csv'], np, ','); % number of paths % File #2: cmX_imr.csv can open with Excel % n'th pair of columns contains the (time,value) pairs for the n'th realization th_ct = zeros(size(t_ct,1),2*size(t_ct,2)); th_ct(:,1:2:end) = t_ct; % odd columns are time th_ct(:,2:2:end) = h_ct; % even columns are values fid = fopen([save_fn '.csv'], 'w'); if fid < 0, error('unable to write .csv file for impulse response, file may be open in another application'); end for k = 1:size(th_ct,1) fprintf(fid,'%.4f,%.6f,', th_ct(k,1:end)); fprintf(fid,'\r\n'); % \r\n for Windoze end-of-line end fclose(fid);