suzuki_type_i.m

《移动衰落信道》Mobiel_Fading_Channels一书后面的相关仿真程序。

%-------------------------------------------------------------------- 
% Suzuki_Type_I.m --------------------------------------------------- 
% 
% Program for the simulation of deterministic extended Suzuki  
% processes of Type I (see Fig. 6.9). 
%  
% Used m-files: parameter_Jakes.m, parameter_Gauss.m, Mu_i_t.m 
%-------------------------------------------------------------------- 
% eta_t=Suzuki_Type_I(N_1,N_2,N_3,sigma_0_2,kappa_0,f_max,sigma_3,... 
%                     m_3,rho,f_rho,theta_rho,f_c,T_s,T_sim,PLOT) 
%-------------------------------------------------------------------- 
% Explanation of the input parameters: 
% 
% N_1, N_2, N_3: number of harmonic functions of the real deter- 
%                ministic Gaussian processes nu_1(t), nu_2(t),  
%                and nu_3(t), respectively 
% sigma_0_2: average power of the real deterministic Gaussian  
%            processes mu_1(t) and mu_2(t) 
% kappa_0: frequency ratio f_min/f_max (0<=kappa_0<=1) 
% f_max: maximum Doppler frequency 
% sigma_3: square root of the average power of the real deterministic 
%          Gaussian process nu_3(t) 
% m_3: average value of the third real deterministic Gaussian  
%      process mu_3(t) 
% rho: amplitude of the LOS component m(t) 
% f_rho: Doppler frequency of the LOS component m(t) 
% theta_rho: phase of the LOS component m(t) 
% f_c: 3-dB-cut-off frequency 
% T_s: sampling interval 
% T_sim: duration of the simulation 
% PLOT: plot of the deterministic extended Suzuki process eta(t) of 
%       Type I, if PLOT==1  
 
function eta_t=Suzuki_Type_I(N_1,N_2,N_3,sigma_0_2,kappa_0,f_max,... 
               sigma_3,m_3,rho,f_rho,theta_rho,f_c,T_s,T_sim,PLOT) 
 
if nargin==14, 
   PLOT=0; 
end 
 
[f1,c1,th1]=parameter_Jakes('es_j',N_1,sigma_0_2,f_max,'rand',0); 
c1=c1/sqrt(2); 
 
N_2_s=ceil(N_2/(2/pi*asin(kappa_0))); 
[f2,c2,th2]=parameter_Jakes('es_j',N_2_s,sigma_0_2,f_max,'rand',0); 
f2 =f2(1:N_2); 
c2 =c2(1:N_2)/sqrt(2); 
th2=th2(1:N_2); 
 
[f3,c3,th3]=parameter_Gauss('es_g',N_3,1,f_max,f_c,'rand',0); 
gaMma=(2*pi*f_c/sqrt(2*log(2)))^2; 
f3(N_3)=sqrt(gaMma*N_3/(2*pi)^2-sum(f3(1:N_3-1).^2)); 
 
N=ceil(T_sim/T_s); 
t=(0:N-1)*T_s; 
 
arg=2*pi*f_rho*t+theta_rho; 
 
xi_t=abs(Mu_i_t(c1,f1,th1,T_s,T_sim)+... 
         Mu_i_t(c2,f2,th2,T_s,T_sim)+rho*cos(arg)+... 
         j*(Mu_i_t(c1,f1,th1-pi/2,T_s,T_sim)-... 
         Mu_i_t(c2,f2,th2-pi/2,T_s,T_sim)+rho*sin(arg))); 
lambda_t=exp(Mu_i_t(c3,f3,th3,T_s,T_sim)*sigma_3+m_3); 
 
eta_t=xi_t.*lambda_t; 
 
if PLOT==1, 
   plot(t,20*log10(eta_t),'b-') 
   xlabel('t (s)') 
   ylabel('20 log eta(t)') 
end