uwbchannel.rar_802.15.3a_超宽带信道

% S-V channel model evaluation % This file has been adapted from code downloaded from the following site: % http://grouper.ieee.org/groups/802/15/pub/2003/Mar03/ % 02490r1P802-15_SG3a-Channel-Modeling-Subcommittee-Report-Final.zip % Omitted clear line, so can run within function % clear; no_output_files = 0; % non-zero: avoids writing output files of continuous-time responses ts = 0.167; % sampling time (nsec) ts=0.05; num_channels = 1000; % 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 % Omit cm_num setting, so can run within function cm_num = 1; % 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 if N > 1, h = resample(hN, 1, N); % decimate the columns of hN by factor N else h = hN; end % correct for 1/N scaling imposed by decimation h = h * N; % 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); showFigures = 0; if showFigures, 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) end % if showFigures 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'); outcsv = 0; if outcsv, % 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); end return; % end of program