MATLAB-Programs.rar_SUZUKI_Suzuki模型_jakeschannel_jakesmodel

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MATLAB-Programs.rar （2个子文件）

MATLAB-Programs.pdf 169KB

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347

MATLAB-PROGRAMS

In the following, a selection of MATLAB-programs (m-ﬁles) is presented, which

save the user from programming eﬀort during the realization of the methods used

to design the model parameters of deterministic processes, and which will help him

ﬁnd his way in the topic of simulation and analysis of mobile radio channel models.

MATLAB stands for matrix laboratory, an interpreter language developed by The

Math Works, Inc., for the numerical computation and visualization of matrices. The

m-ﬁles presented below require the Signal Processing Toolbox and the Optimization

Toolbox.

References on necessary subroutines (functions) and a description of the input and

output parameters of the individual programs can be found in the program header

block of each program.

At ﬁrst, the m-ﬁles for the computation of the model parameters are presented

by making use of the methods describ ed in Chapter 5. Here, these methods are

subdivided depending on the type of power spectral density (Jakes/Gauss) of the

deterministic Gaussian processes to be realized. Subsequently, functions for the

time-domain simulation of various frequency-nonselective (Chapter 6) and frequency-

selective (Chapter 7) mobile radio channels are presented. Finally, further tools are

provided, with the help of which the designed channel simulators can be analysed

with respect to their statistical properties such as the probability density function,

the cumulative distribution function, the level-crossing rate, and the average duration

of fades.

%--------------------------------------------------------------------

% parameter_Jakes.m -------------------------------------------------

% Program for the computation of the discrete Doppler frequencies,

% Doppler coefficients and Doppler phases by using the Jakes power

% spectral density.

% Used m-files: LPNM_opt_Jakes.m, fun_Jakes.m,

% grad_Jakes.m, acf_mue.m

%--------------------------------------------------------------------

% [f_i_n,c_i_n,theta_i_n]=parameter_Jakes(METHOD,N_i,sigma_0_2,...

Mobile Fading Channels. Matthias P¨atzold

 2002 John Wiley & Sons, Ltd.

ISBN: 0-471-49549-2

348 MATLAB-PROGRAMS

% f_max,PHASE,PLOT)

%--------------------------------------------------------------------

% Explanation of the input parameters:

% METHOD:

% |----------------------------------------------|------------------|

% | Methods for the computation of the discrete | Input |

% | Doppler frequencies and Doppler coefficients | |

% |----------------------------------------------|------------------|

% | Method of equal distances (MED) | ’ed_j’ |

% |----------------------------------------------|------------------|

% | Mean square error method (MSEM) | ’ms_j’ |

% |----------------------------------------------|------------------|

% | Method of equal areas (MEA) | ’ea_j’ |

% |----------------------------------------------|------------------|

% | Monte Carlo method (MCM) | ’mc_j’ |

% |----------------------------------------------|------------------|

% | Lp-norm method (LPNM) | ’lp_j’ |

% |----------------------------------------------|------------------|

% | Method of exact Doppler spread (MEDS) | ’es_j’ |

% |----------------------------------------------|------------------|

% | Jakes method (JM) | ’jm_j’ |

% |----------------------------------------------|------------------|

% N_i: number of harmonic functions

% sigma_0_2: average power of the real deterministic Gaussian

% process mu_i(t)

% f_max: maximum Doppler frequency

% PHASE:

% |----------------------------------------------|------------------|

% | Methods for the computation of the Doppler | Input |

% | phases | |

% |----------------------------------------------|------------------|

% | Random Doppler phases | ’rand’ |

% |----------------------------------------------|------------------|

% | Permuted Doppler phases | ’perm’ |

% |----------------------------------------------|------------------|

% PLOT: plot of the ACF and the PSD of mu_i(t), if PLOT==1

function [f_i_n,c_i_n,theta_i_n]=parameter_Jakes(METHOD,N_i,...

sigma_0_2,f_max,PHASE,PLOT)

if nargin<6,

MATLAB-PROGRAMS 349

error(’Not enough input parameters’)

end

sigma_0=sqrt(sigma_0_2);

% Method of equal distances (MED)

if METHOD==’ed_j’,

n=(1:N_i)’;

f_i_n=f_max/(2*N_i)*(2*n-1);

c_i_n=2*sigma_0/sqrt(pi)*(asin(n/N_i)-asin((n-1)/N_i)).^0.5;

K=1;

% Mean square error method (MSEM)

elseif METHOD==’ms_j’,

n=(1:N_i)’;

f_i_n=f_max/(2*N_i)*(2*n-1);

Tp=1/(2*f_max/N_i);

t=linspace(0,Tp,5E3);

Jo=besselj(0,2*pi*f_max*t);

c_i_n=zeros(size(f_i_n));

for k=1:length(f_i_n),

c_i_n(k)=2*sigma_0*...

sqrt(1/Tp*( trapz( t,Jo.*...

cos(2*pi*f_i_n(k)*t )) ));

end

K=1;

% Method of equal areas (MEA)

elseif METHOD==’ea_j’

n=(1:N_i)’;

f_i_n=f_max*sin(pi*n/(2*N_i));

c_i_n=sigma_0*sqrt(2/N_i)*ones(size(n));

K=1;

% Monte Carlo method (MCM)

elseif METHOD==’mc_j’

n=rand(N_i,1);

f_i_n=f_max*sin(pi*n/2);

c_i_n=sigma_0*sqrt(2/N_i)*ones(size(n));

K=1;

% Lp-norm method (LPNM)

elseif METHOD==’lp_j’,

if exist(’fminu’)~=2

disp([’ =====> This method requires ’,...

’the Optimization Toolbox !!’])

350 MATLAB-PROGRAMS

return

else

N=1E3;

p=2; % Norm

s_o=1;

[f_i_n,c_i_n]=LPNM_opt_Jakes(N,f_max,sigma_0,p,N_i,s_o);

K=1;

end

% Method of exact Doppler spread (MEDS)

elseif METHOD==’es_j’,

n=(1:N_i)’;

f_i_n=f_max*sin(pi/(2*N_i)*(n-1/2));

c_i_n=sigma_0*sqrt(2/(N_i))*ones(size(f_i_n));

K=1;

% Jakes method (JM)

elseif METHOD==’jm_j’,

n=1:N_i-1;

f_i_n=f_max*[[cos(pi*n/(2*(N_i-1/2))),1]’,...

[cos(pi*n/(2*(N_i-1/2))),1]’];

c_i_n=2*sigma_0/sqrt(N_i-1/2)*[[sin(pi*n/(N_i-1)),1/2]’,...

[cos(pi*n/(N_i-1)),1/2]’];

K=1;

theta_i_n=zeros(size(f_i_n));

PHASE=’none’;

else

error(’Method is unknown’)

end

% Computation of the Doppler phases:

if PHASE==’rand’,

theta_i_n=rand(N_i,1)*2*pi;

elseif PHASE==’perm’,

n=(1:N_i)’;

Z=rand(size(n));

[dummy,I]=sort(Z);

theta_i_n=2*pi*n(I)/(N_i+1);

end;

if PLOT==1,

if METHOD==’jm_j’

subplot(2,3,1)

stem([-f_i_n(N_i:-1:1,1);f_i_n(:,1)],...

1/4*[c_i_n(N_i:-1:1,1);c_i_n(:,1)].^2)

MATLAB-PROGRAMS 351

title(’i=1’)

xlabel(’f (Hz)’)

ylabel(’PSD’)

subplot(2,3,2)

stem([-f_i_n(N_i:-1:1,2);f_i_n(:,2)],...

1/4*[c_i_n(N_i:-1:1,2);c_i_n(:,2)].^2)

title(’i=2’)

xlabel(’f (Hz)’)

ylabel(’PSD’)

tau_max=N_i/(K*f_max);

tau=linspace(0,tau_max,500);

r_mm=sigma_0^2*besselj(0,2*pi*f_max*tau);

r_mm_tilde1=acf_mue(f_i_n(:,1),c_i_n(:,1),tau);

subplot(2,3,4)

plot(tau,r_mm,’r-’,tau,r_mm_tilde1,’g--’)

title(’i=1’)

xlabel(’tau (s)’)

ylabel(’ACF’)

r_mm_tilde2=acf_mue(f_i_n(:,2),c_i_n(:,2),tau);

subplot(2,3,5)

plot(tau,r_mm,’r-’,tau,r_mm_tilde2,’g--’)

title(’i=2’)

xlabel(’tau (s)’)

ylabel(’ACF’)

subplot(2,3,3)

stem([-f_i_n(N_i:-1:1,1);f_i_n(:,1)],...

1/4*[c_i_n(N_i:-1:1,1);c_i_n(:,1)].^2+...

1/4*[c_i_n(N_i:-1:1,2);c_i_n(:,2)].^2)

title(’i=1,2’)

xlabel(’f (Hz)’)

ylabel(’PSD’)

subplot(2,3,6)

plot(tau,2*r_mm,’r-’,tau,r_mm_tilde1+r_mm_tilde2,’g--’)

title(’i=1,2’)

xlabel(’tau (s)’)

ylabel(’ACF’)

else

subplot(1,2,1)

stem([-f_i_n(N_i:-1:1);f_i_n],...

1/4*[c_i_n(N_i:-1:1);c_i_n].^2)

xlabel(’f/Hz’)

ylabel(’LDS’)

tau_max=N_i/(K*f_max);

tau=linspace(0,tau_max,500);

r_mm=sigma_0^2*besselj(0,2*pi*f_max*tau);

r_mm_tilde=acf_mue(f_i_n,c_i_n,tau);

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