% mapping qpsk
for i=1:2:length(r)
instead of the word ‘phase’ in the above line, it should be amplitude of the basis functions depends on the message symbol. the linear combination of the basis functions in turn results in the output function whose phase depends on the message symbol.
]]>Hi…Nice blog…
One question…How to extend this to 8 PSK?
Hello,
This link gives the idea of using different modulation schemes for your bit stream and the important Bir Error Rate curve which is also used in practice:
https://www.gaussianwaves.com/2010/04/performance-comparison-of-digital-modulation-techniques-2/
The figure in this link shows well the more you increase the symbols (BPSK, QPSK, 8PSK , …) the more the probability of error is going to be increased. Becasue simply there is more bits to be wrong. So for a fixed Power (SNR) you have a better performance for BPSK.
But in the cost that your bit rate is the smallest possible in your system.
otherwise you can have maximum bit rate.
i have Q
can u help me
Digital information is to be transmitted by digital modulation through an additive white
Gaussian noise channel with a required bit error probability, Pb of 2×10-4. Evaluate the
dependency of the bit error probability, Pb, on Eb/N0 in dB, of the modulation schemes DPSK
and 8-PSK. If the system’s main criterion is the bit error probability, Pb, which of these
modulation schemes would you choose? Provide your answer by showing calculations.
Actually, Just for checking a Theoretical important problem, Square Root Raised Cosine filter does not have a relation with your NRZ Stream, it’s just a pulse shaping of your NRZ Stream it means that you wanna send for example 1 -1 1 -1 -1 1 , … then the signal analogue that specifies to 1 will be 1 * Cosine filter and for -1 will be (-1)* Cosine wave, … which effectively depends on your sampling frquency of filter that you have choosen and the rol of factor of pulse,
So my solution for you is at first upsample of your NRZ data arbiary (4,8 or 16 , …) and then taking convolution of upsampled data and root raised cosine filter will have give simply the output that you need. and now it’s your ready signal to modulation.
Matlab code :
N = 10^4 ; % number of data
up = 4; % upsampling data
d = randsrc(1,N);
du = upsample (d,up);
x = conv(du,Root_Raised_Cosine); % your ready signal to go to modulate