uwb_sv_model_ct_15_4a.m

IEEE802.15.4a信道模型-matlab实现,超宽带研究者的必备代码~

function [h,t,t0,np] = uwb_sv_model_ct_15_4a(Lam,lambda,Lmean,lambda_mode,lambda_1, ...
    lambda_2,beta,Gam,gamma_0,Kgamma,sigma_cluster,nlos,gamma_rise,gamma_1, ...
    chi,m0,Km,sigma_m0,sigma_Km,sfading_mode,m0_sp,std_shdw,num_channels,ts)
% Written by Sun Xu, Kim Chee Wee, B. Kannan & Francois Chin on 14/09/2004  
%   IEEE 802.15.4a UWB channel model for PHY proposal evaluation
%      continuous-time realization of modified S-V channel model
% Input parameters:
%   detailed introduction of input parameters is at uwb_sv_params.m
%   num_channels  number of random realizations to generate
% Outputs
%   h is returned as a matrix with num_channels columns, each column 
%     holding a random realization of the channel model (an impulse response)
%   t is organized as h, but holds the time instances (in nsec) of the paths whose
%     signed amplitudes are stored in h
%   t0 is the arrival time of the first cluster for each realization
%   np is the number of paths for each realization.
% Thus, the k'th realization of the channel impulse response is the sequence 
%   of (time,value) pairs given by (t(1:np(k),k), h(1:np(k),k))
%
% modified by I2R


% initialize and precompute some things
std_L = 1/sqrt(2*Lam);      % std dev (nsec) of cluster arrival spacing
std_lam = 1/sqrt(2*lambda); % std dev (nsec) of ray arrival spacing
h_len = 1000;  % there must be a better estimate of # of paths than this
ngrow = 1000; % amount to grow data structure if more paths are needed
h = zeros(h_len,num_channels);
t = zeros(h_len,num_channels);
t0 = zeros(1,num_channels);
np = zeros(1,num_channels);

for k = 1:num_channels       % loop over number of channels
  tmp_h = zeros(size(h,1),1);
  tmp_t = zeros(size(h,1),1);
  if nlos == 1,
    Tc = (std_L*randn)^2 + (std_L*randn)^2;  % First cluster random arrival
  else
    Tc = 0;                   % First cluster arrival occurs at time 0
  end
  t0(k) = Tc;
  
  if nlos == 2 & lambda_mode == 2
      L = 1;  % for industrial NLOS environment
  else
      L = max(1, poissrnd(Lmean));       % number of clusters
  end

  cluster_index = zeros(1,L);
  path_ix = 0;
  nak_m = [];
  for ncluster = 1:L
    % Determine Ray arrivals for each cluster
    Tr = 0;  % first ray arrival defined to be time 0 relative to cluster
    cluster_index(ncluster) = path_ix+1;  % remember the cluster location
    gamma = Kgamma*Tc + gamma_0; % delay dependent cluster decay time
    if nlos == 2 & ncluster == 1
      gamma = gamma_1;
    end
    Mcluster = sigma_cluster*randn;
    Pcluster = 10*log10(exp(-1*Tc/Gam))+Mcluster; % total cluster power
    Pcluster = 10^(Pcluster*0.1);
    while (Tr < 10*gamma),
      t_val = (Tc+Tr);  % time of arrival of this ray
      if nlos == 2 & ncluster == 1
        h_val = Pcluster*(1-chi*exp(-Tr/gamma_rise))*exp(-Tr/gamma_1) ...
               *(gamma+gamma_rise)/gamma/(gamma+gamma_rise*(1-chi));
      else
        h_val = Pcluster/gamma*exp(-Tr/gamma);
      end
      path_ix = path_ix + 1;  % row index of this ray
      if path_ix > h_len,
        % grow the output structures to handle more paths as needed
        tmp_h = [tmp_h; zeros(ngrow,1)];
        tmp_t = [tmp_t; zeros(ngrow,1)];
        h = [h; zeros(ngrow,num_channels)];
        t = [t; zeros(ngrow,num_channels)];
        h_len = h_len + ngrow;
      end
      tmp_h(path_ix) = h_val;
      tmp_t(path_ix) = t_val;
      if lambda_mode == 0
        Tr = Tr + (std_lam*randn)^2 + (std_lam*randn)^2;
      elseif lambda_mode == 1
        if rand < beta
          std_lam = 1/sqrt(2*lambda_1);
          Tr = Tr + (std_lam*randn)^2 + (std_lam*randn)^2;
        else
          std_lam = 1/sqrt(2*lambda_2);
          Tr = Tr + (std_lam*randn)^2 + (std_lam*randn)^2;
        end
      elseif lambda_mode == 2
        Tr = Tr + ts;
      else
        error('lambda mode is wrong!')
      end
      % generate log-normal distributed nakagami m-factor
      m_mu = m0 - Km*t_val;
      m_std = sigma_m0 - sigma_Km*t_val;
      nak_m = [nak_m, lognrnd(m_mu, m_std)];
    end
    Tc = Tc + (std_L*randn)^2 + (std_L*randn)^2;
  end

  % change m value of the first multipath to be the deterministic value
  if sfading_mode == 1
    nak_ms(cluster_index(1)) = m0_sp;
  elseif sfading_mode == 2
    nak_ms(cluster_index) = m0_sp;
  end
  
  % apply nakagami
  for path = 1:path_ix
    h_val = (gamrnd(nak_m(path), tmp_h(path)/nak_m(path))).^(1/2);
    tmp_h(path) = h_val;
  end

  np(k) = path_ix;  % number of rays (or paths) for this realization
  [sort_tmp_t,sort_ix] = sort(tmp_t(1:np(k)));    % sort in ascending time order
  t(1:np(k),k) = sort_tmp_t;
  h(1:np(k),k) = tmp_h(sort_ix(1:np(k)));
  % now impose a log-normal shadowing on this realization
%   fac = 10^(std_shdw*randn/20) / sqrt( h(1:np(k),k)' * h(1:np(k),k) );
%   h(1:np(k),k) = h(1:np(k),k) * fac;
end
return