%% Eye Diagram (eyediagram) and Scatter Plot (scatterplot) Functions
% This is a demonstration of the Communications Toolbox functions EYEDIAGRAM and
% SCATTERPLOT.  It shows how to use the functions in analyzing communication
% systems.

% Copyright 1996-2004 The MathWorks, Inc.
% $Revision: 1.16.4.6 $ $Date: 2004/12/18 07:33:35 $

%% Using the functions EYEDIAGRAM and SCATTERPLOT
% Plotting a modulated, filtered signal using PLOT does not display the
% characteristics of the modulation as clearly as the eye diagram and scatter
% plot.  The eye diagram overlays many short segments, called traces, to reveal
% the signal characteristics.  The scatter plot samples the signal at the symbol
% time and displays it in signal space.  The sampling rate of 16 highlights the
% plotting functions' capability. 

N = 16; Fd = 1; Fs = N * Fd; Delay = 3; Symb = 60; offset=0; M = 16;
msg_orig = randsrc(Symb,1,[0:M-1],4321);
msg_tx = qammod(msg_orig,M);
[y, t] = rcosflt(msg_tx, Fd, Fs);
plot(t, real(y));

%%
% EYEDIAGRAM is used to plot multiple traces of a modulated, filtered signal to
% demonstrate the shape of the modulation.  There is one plot for the in-phase
% component and another for the quadrature component.

% Create the eye diagram.
h1 = eyediagram(y, N);

% Move the figure to the right-hand side of the screen.
ss = get(0,'ScreenSize');
fp = get(h1,'position');
set(h1,'position',[5 ss(4)*.9-fp(4) fp(3:4)]);

%%
% EYEDIAGRAM can also be used to plot a real signal.  yReal is the real or
% in-phase component of y.  EYEDIAGRAM senses that the signal is real and only
% creates one pair of axes.

% Create the eye diagram.
yReal = real(y);
h2 = eyediagram(yReal, N);

% Manage the figures.
close(h1(ishandle(h1)))
fp = get(h2,'position');
set(h2,'position',[5 ss(4)*.9-fp(4) fp(3:4)]);


%%
% EYEDIAGRAM in the previous slides plotted all of y.  However, we are not
% interested in plotting the tails of y, which contain the early and late
% partial responses of the raised cosine filter.  This slide demonstrates the
% eye diagram of yy, which does not include the tails of y.  Now the
% characteristics of the modulation are evident.  The in-phase and quadrature
% phase components of the 16-ary QAM each take on 4 distinct levels at the
% center of the eye plot and smoothly transition between levels.

% Create the eye diagram.
yy = y(1+Delay*N:end-Delay*(N+2));
h3 = eyediagram(yy, N);

% Manage the figures.
close(h2(ishandle(h2)))
fp = get(h3,'position');
set(h3,'position',[5 ss(4)*.9-fp(4) fp(3:4)]);

%%
% EYEDIAGRAM can also plot multiple symbols in a trace.  The figure shows
% EYEDIAGRAM plotting two symbols per trace. 

% Create the eye diagram.
NumSym = 2;
h4 = eyediagram(yy, N*NumSym, NumSym);

% Manage the figures.
close(h3(ishandle(h3)))
fp = get(h4,'position');
set(h4,'position',[5 ss(4)*.9-fp(4) fp(3:4)]);


%%
% You may adjust the eye diagram so the open part of the eye is not at the
% center of the axes.  This code shows how EYEDIAGRAM can be called to plot the
% closed part of the eye in the center of the axes.

% Create the eye diagram.
h5 = eyediagram(yy, N, 1, N/2);

% Manage the figures.
close(h4(ishandle(h4)))
fp = get(h5,'position');
set(h5,'position',[5 ss(4)*.9-fp(4) fp(3:4)]);


%%
% SCATTERPLOT generates a scatter plot, which is a plot of the in-phase versus
% quadrature components of the signal, decimated by a factor that is usually set
% to the number of samples per symbol, N.  The resulting plot shows the received
% signal sampled at the symbol rate.  When the channel is noiseless and
% distortionless, the symbols are identical to the information symbols at the
% transmitter.
 
% Create the scatter plot.
h6 = scatterplot(yy, N);

% Manage the figures.
close(h5(ishandle(h5)));
fp = get(h6,'position');
set(h6,'position',[5 ss(4)*.9-fp(4) fp(3:4)]);

%%
% SCATTERPLOT is executed twice to generate this plot.  The cyan line represent
% the in-phase versus the quadrature component of the received signal trajectory
% in signal space.  The blue dots represent the signal at the symbol sampling
% times.  This plot demonstrates that, even though the signal moves about in
% signal space, its value equals the value of the original signal when sampled
% at the symbol boundaries. 

% Create the scatter plot.
h7 = scatterplot(yy, 1, 0, 'c-');
hold on;
scatterplot(yy, N, 0, 'b.',h7);
hold off;

% Manage the figures.
close(h6(ishandle(h6)));
fp = get(h7,'position');
set(h7,'position',[5 ss(4)*.9-fp(4) fp(3:4)]);


%% Using these functions in analyzing communication systems
% This code plots the signal with noise added to it (red line).  Even though the
% noisy signal can be differentiated from the original signal (blue line), it is
% still difficult to determine the effect the noise has on the signal.  The
% signal to noise ratio (SNR) for the noisy signal is 15 dB.

% Close the figure.
close(h7(ishandle(h7)));

% Create the plot.
SNR = 15;
sig_rx = awgn(msg_tx,SNR,'measured',1234,'dB');
[fsig_rx, t2] = rcosflt(sig_rx, Fd, Fs);
tfsig_rx = fsig_rx(1+Delay*N:end-Delay*(N+1),:);
plot(t, real(y),'b-', t2, real(fsig_rx),'r-');


%%
% These figures are eye diagrams of the original signal (blue lines) and the
% noisy signal (red lines).  The eye diagrams clearly demonstrate the variation
% in the received signal from the transmitted signal due to the noise added to
% the signal. 

% Create the eye diagrams.
h(1) = eyediagram(yy, N,1);
h(2) = eyediagram(tfsig_rx, N,1,0,'r-');

% Move the eye diagram figures to the side of the screen.
fp = get(h(1),'position');
set(h(1),'position',[5 ss(4)*.9-fp(4) fp(3) fp(4)]);
set(h(2),'position',[5+fp(3)/2 ss(4)*.75-fp(4) fp(3) fp(4)]);

%%
% These figures are scatter plots of the original signal (blue dots) and the
% noisy signal (red dots).  The scatter plots clearly demonstrate the variation
% from the transmitted signal in the received signal at the symbol boundaries. 

% Create the scatter plots.
h(3) = scatterplot(yy, N, 0, 'b.');
h(4) = scatterplot(tfsig_rx, N, 0, 'r.');

% Move the scatter plot figures to the bottom of the screen.
fp = get(h(3),'position');
set(h(3),'position',[ss(1) ss(4)*0.05 fp(3) fp(4)]);
set(h(4),'position',[ss(1)+fp(3)/2 ss(4)*0.05 fp(3) fp(4)]);

%%
% This is a plot of the same transmitted signal received at a SNR of 20 dB
% (magenta line).  The variations caused by noise are smaller than with the
% previous signal (SNR = 15 dB).  In this case the eye diagrams and scatter
% plots are needed even more to determine the modulation characteristics.

% Close all open figures.
close(h(ishandle(h)));

% Plot the transmitted signal.
SNR = 20;
sig_rx = awgn(msg_tx,SNR,'measured',4321,'dB');
[fsig_rx2, t2] = rcosflt(sig_rx, Fd, Fs);
tfsig_rx2 = fsig_rx2(1+Delay*N:end-Delay*(N+1),:);
plot(t, real(y),'b-', t2, real(fsig_rx2), 'm-');

%%
% These figures are eye diagrams of the first noisy signal (red lines) and the
% second noisy signal (magenta lines).  The eye diagrams clearly demonstrate
% that the variation in the second received signal is less than the first
% received signal.  The two noisy signals are also plotted for reference. 

% Create the plots.
plot(t, real(fsig_rx),'r-',t2,real(fsig_rx2),'m-');
h(1) = eyediagram(tfsig_rx, N,1,0,'r-');
h(2) = eyediagram(tfsig_rx2, N,1,0,'m-');

% Manage the figures.
fp = get(h(1),'position');
set(h(1),'position',[5 ss(4)*.9-fp(4) fp(3) fp(4)]);
set(h(2),'position',[5+fp(3)/2 ss(4)*.75-fp(4) fp(3) fp(4)]);

%%
% These figures are scatter plots of the first noisy signal (red dots) and the
% second noisy signal (magenta dots).  The scatter plots clearly demonstrate the
% variation from the second received signal is less than the first received
% signal at the sampling points.

% Create the scatterplots.
h(3) = scatterplot(tfsig_rx, N, 0, 'r.');
h(4) = scatterplot(tfsig_rx2, N, 0, 'm.');

% Manage the figures.
fp = get(h(3),'position');
set(h(3),'position',[ss(3)*.01 ss(4)*.05 fp(3) fp(4)]);
set(h(4),'position',[ss(3)*.3 ss(4)*.05 fp(3) fp(4)]);

%% Using animation to visualize the impact of timing errors
% This animation of the eye diagram and scatter plot shows that they can be
% plotted with any offset.

% Close all open figures.
close(h(ishandle(h)));

% Create and animate the plots.
plot(t2,real(fsig_rx2),'m-');
[h(1), h(2)] = animatescattereye(tfsig_rx2,N,.1,N,'lin',-1);


%%
% This animation of the eye diagram and scatter plot approximates the effect of
% timing offsets at the receiver.   The offset parameter in EYEDIAGRAM and
% SCATTERPLOT are varied about the correct sampling time.   This animates the
% impact  in time and in signal space of sampling the received signal before and
% after the correct sampling time.

% Close all open figures.
close(h(ishandle(h)));

% Create and animate the plots.
plot(t2,real(y),'b-');
animatescattereye(yy,N,.1,17,'lr',0);