Kindly see the code below if its helps:

clear all

%% Define Parameters

% sampling frequency (Hz)

fs=100e6;

% length of time-domain signal

L=30e3;

% desired power specral density (dBm/Hz)

Pd=-100;

% number of FFT points

nfft=2^nextpow2(L);

% frequency plotting vector

f=fs/2*[-1:2/nfft:1-2/nfft];

% create

s=wgn(L,1,Pd+10*log10(fs),1,[],'dBm','complex');

%% Analysis

% analyze spectrum

N=nfft/2+1:nfft;

S=fftshift(fft(s,nfft));

S=abs(S)/sqrt(L*fs);

% time-average for spectrum

Navg=4e2;

b(1:Navg)=1/Navg;

Sa=filtfilt(b,1,S);

% convert to dBm/Hz

S=20*log10(S)+30;

Sa=20*log10(Sa)+30;

%% Plot

figure(1)

clf

plot(f(N)/1e6,S(N))

hold on

plot(f(N)/1e6,Sa(N),'r')

xlabel('Frequency (MHz)')

ylabel('Power Density (dBm/Hz)')

title(['Power Spectral Density'])

legend('Noise Spectrum','Time-Averaged Spectrum')

axis([10e-4 fs/2/1e6 -120 -60])

grid on

hold off

Source: http://www.bitweenie.com/listings/power-spectral-density-matlab/

http://www.bitweenie.com/listings/power-spectral-density-matlab/

Power Spectral Density Estimates Using FFT

Dear, check the following link, may help you:

http://www.mathworks.com/examples/signal/mw/signal-ex08634157-power-spectral-density-estimates-using-fft

Try below code:

note:Fs depends upon data

[k,col]=size(data);

fft_samps = 2^nextpow2(k);

t_fft = fft (data, fft_samps)/k;

Fs=--------;

fprime = Fs/2*linspace(0,1,fft_samps/2);

plot(fprime,2*abs(t_fft(1:fft_samps/2)));