www.pudn.com > tryGUI.rar > uwb_sv_eval_ct.m

% S-V channel model evaluation 
clear; 
 
no_output_files = 0;  % non-zero: avoids writing output files of continuous-time responses 
ts = 0.167;             % sampling time (nsec) 
num_channels = 1 ;      % 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_num = 4;  % channel model number from 1 to 4 
% get channel model params based on this channel model number 
[Lam,lambda,Gam,gamma,std_ln_1,std_ln_2,nlos,std_shdw] = uwb_sv_params( cm_num ); 
fprintf(1,['Model Parameters\n' ... 
  '  Lam = %.4f, lambda = %.4f, Gam = %.4f, gamma = %.4f\n' ... 
  '  std_ln_1 = %.4f, std_ln_2 = %.4f, NLOS flag = %d, std_shdw = %.4f\n'], ... 
  Lam, lambda, Gam, gamma, std_ln_1, std_ln_2, nlos, std_shdw); 
 
% get a bunch of realizations (impulse responses) 
[h_ct,t_ct,t0,np] = uwb_sv_model_ct( Lam, lambda, Gam, gamma, std_ln_1, std_ln_2, nlos, ... 
                  std_shdw, num_channels ); 
% now reduce continuous-time result to a discrete-time result 
[hN,N] = uwb_sv_cnvrt_ct( h_ct, t_ct, np, num_channels, ts ); 
% if we wanted complex baseband model or to impose some filtering function, 
% this would be a good place to do it 
N=8; 
figure(12); 
plot(hN); 
if N > 1, 
  %h = resample(hN, 1, N);  % decimate the columns of hN by factor N 
  h=decimate(hN,N); 
else 
  h = hN; 
end 
% correct for 1/N scaling imposed by decimation 
h = h * N; 
 
 
figure(13); 
plot(h); 
hold on 
stem(downsample(hN,N),'r'); 
hold off; 
 
% channel energy 
channel_energy = sum(abs(h).^2); 
 
h_len = size(h,1); 
t = [0:(h_len-1)] * ts;  % for use in computing excess & RMS delays 
excess_delay = zeros(1,num_channels); 
RMS_delay = zeros(1,num_channels); 
num_sig_paths = zeros(1,num_channels); 
num_sig_e_paths = zeros(1,num_channels); 
for k=1:num_channels 
  % 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 number 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); 
 
figure(1); clf; plot(t,h); grid on 
title('Impulse response realizations') 
xlabel('Time (nS)') 
 
figure(2); clf; plot([1:num_channels], excess_delay, 'b-', ... 
  [1 num_channels], mean_excess_delay*[1 1], 'r--' ); 
grid on 
title('Excess delay (nS)') 
xlabel('Channel number') 
 
figure(3); clf; plot([1:num_channels], RMS_delay, 'b-', ... 
  [1 num_channels], mean_RMS_delay*[1 1], 'r--' ); 
grid on 
title('RMS delay (nS)') 
xlabel('Channel number') 
 
figure(4); clf; plot([1:num_channels], num_sig_paths, 'b-', ... 
  [1 num_channels], mean_sig_paths*[1 1], 'r--'); 
grid on 
title('Number of significant paths within 10 dB of peak') 
xlabel('Channel number') 
 
figure(5); clf; plot([1:num_channels], num_sig_e_paths, 'b-', ... 
  [1 num_channels], 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_channels; 
temp_average_power = temp_average_power/max(temp_average_power); 
average_decay_profile_dB = 10*log10(temp_average_power); 
figure(6); clf; 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)') 
 
figure(7); clf 
figh = plot([1:num_channels],10*log10(channel_energy),'b-', ... 
  [1 num_channels], energy_mean*[1 1], 'g--', ... 
  [1 num_channels], energy_mean+energy_stddev*[1 1], 'r:', ... 
  [1 num_channels], 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_num); 
 
% A complete self-contained file for Matlab users 
save([save_fn '.mat'], 't_ct', 'h_ct', 't0', 'np', 'num_channels', 'cm_num'); 
 
% 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-2)); 
  fprintf(fid,'%.4f,%.6f\r\n', th_ct(k,end-1:end)); % \r\n for Windoze end-of-line 
end 
fclose(fid); 
 
return; % end of program