Accurate description of the spectrum of sinusoid waveform added with WGN? The transmitting signal is
$$
s(t) = \sin(20\pi t)+\sin(40\pi t)+\sin(60\pi t)
$$

The received signal after AWGN channel is
$$
r(t) = s(t)+n(t)
$$
where the variance of noise $n(t)$ is $\sigma^2$.

The theoretical single  spectrum of the $r(t)$ should be

$$
R(f) = \pi\delta(f-10)+ \pi\delta(f-20)+ \pi\delta(f-30)+\sigma^2
$$

where $\delta(.)$ is Delta function. However, the spectrum obtained through MATLAB seems not like the theroetical one.



When the SNR is setted as lower than -25dB, the spikes will be submerged by noise. And how to calculate the threshold SNR where sinusoid part is totally covered by noise?



The relative code is here

fs_init = 10; % basis frequency
om=fs_init*2*pi;
T = 2*pi/om; 
tall = 10;
h=T*0.01;
t=0:h:tall-h;
fs_init= 10;
ut = zeros(1,length(t));

%%% Added constants
NSyboml = 3;
a=1;

for i = 1:NSyboml
    ut = ut+a*sin(2*pi*i*fs_init*t);
end

SNR = 0;
rt = awgn(ut,SNR,'measured');   % add noise

% FFT 
y = rt;
L = length(ut);    % Length of signal
Ts_v = t./(0:L-1); % sample time
Fs = ceil(1/Ts_v(2));    % sample frequency
deltaf = 0.1; % resolution
NFFT = floor(Fs/deltaf);
Y = fft(y,NFFT)/L;
f = Fs/2*linspace(0,1,floor(NFFT/2+1));

% Plot single-sided amplitude spectrum.
 figure;set(gcf,'Color','w','Position',[0 0 500 350]);
plot(f,20*log10(abs(Y(1:NFFT/2+1))));
xlim([0,6*fs_init])
xlabel('Frequency (Hz)');ylabel('|R(f)|');
grid on;
ylim([-120,0])
title(['SNR = ',num2str(SNR),'dB'])
%%%enter preformatted text here
Notice that in your first image, there are peaks where they would be expected to be, so that's fine. I guess you think that "the spectrum obtained through MATLAB seems not like the theroetical one" because of the noisy spectrum you get, instead of a straight line at $\sigma^2$ on the vertical axis.

The random signal added to $s(t)$ is not random actually, it's just a realization you get when calling awgn(). So the FFT will return just a noisy spectrum for that signal. If you generate lots of realizations of $r(t)$ and plot the average of all their FFTs, you'll obtain what you were expecting for.

For your second question on how to calculate the threshold SNR, some criteria is needed. If you average lots of FFTs the variance of the noisy part of the spectrum will drastically decrease, so the SNR needed to see the peaks will be much smaller. If you use just one realization, then the SNR will be higher. There is not a fixed number for the needed SNR, it depends on what you refer to "sinusoid covered by noise". If the spectrum observed was just like the theoretical one (i.e. you averaged a lot of FFTs), a sinusoid would be "completely covered by noise" when its amplitude is such that the corresponding peak in the frequency domain is below $\sigma^2$.