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%Smart Antennas figure 4-11 Binomial weights on a linear array d=.5; N=input('what is the number of elements?'); theta=-pi/2:.01:pi/2; ang=theta*180/pi; test=diag(rot90(pascal(N))); check=mod(N,

%Smart Antennas figure 4.15 Binomial d=.5; N=input('what is the number of elements?'); theta=-pi/2:.01:pi/2; ang=theta*180/pi; test=hamming(N); check=mod(N,2) if check == 0 wB=flipud(test(1:N/

%Smart Antennas figure 4.17 Gaussian weights on linear array d=.5; N=input('what is the number of elements?'); theta=-pi/2:.01:pi/2; ang=theta*180/pi; test=gausswin(N); check=mod(N,2) if check

%Smart Antennas figure 4.13 Blackman weights on a linear array d=.5; N=input('what is the number of elements?'); theta=-pi/2:.01:pi/2; ang=theta*180/pi; test=blackman(N); check=mod(N,2) if chec

%Maximum SIR beamforming % example 8.2 d=.5; N=3; sig2=.001; % noise variance theta=-pi/2:.01:pi/2; ang=theta*180/pi; th0=pi/6; % receive angle th1=-pi/6; % first interfer

%%%%%%%%%%%%%%%%%%% %% Conjugate Gradient %% %%%%%%%%%%%%%%%%%%% %----- Givens -----% K=20; % total number of data samples sig2=.001; d = .5; % element spacing in terms of wavelength d = l

% Smart Antennas figure 3.16 calculate the polar patterns for the finite length dipole and the 3-D patterns % Lolam= L/lambda; th=-pi:.01:pi; lolam=.5:.5:1.5; u1=((cos(pi*lolam(1)*cos(th))-cos(pi*

% Smart Antennas Figure 3.17 plotting the directivity vs. finite dipole length in wavelengths F=inline('((cos(pi*ll*cos(x))-cos(pi*ll))./sin(x)).^2.*sin(x)') delta=.01; x=delta:delta:pi; for i

% Smart antennas figure 3.7. 3-D pattern for cos(theta)^4 pattern % use 100 data points in theta and 100 data points in phi tend=pi/2; set(0,'defaultfigurecolor','w') fx=inline('abs(sin(3*pi*sin

%Godara Method % Example 8.1 d=.5; N=5; sig2=.001 theta=-pi/2:.01:pi/2; ang=theta*180/pi; th0=0; % receive angle th1=-15*pi/180; % first interferer angle th2=25*pi/180;