As asked here, HERE if we have the signal
x =
[0.7 + 0.7i;
0.7 - 0.7i;
-0.7 + 0.7i;
-0.7 -0.7i];
Which was spread over code c
and transmitted over channel H
whose dimension is [4x4], so the convolution of signal after spreading will become:
r = reshape(H*reshape(x,4,[]),[],1);
comparing it with signal without spreading, it was
r = H*x;
That was explained well in the above link.
My question, suppose I am using Maximum likelihood estimation, in case if we didn't spread the signal, we will check the likelhood compared with the channel H
, but what's about after using spreading ? how will become the channel ? It supposed to be a vector of [16x1], is that right ? but how will it be ?
thank you!
Answer
H was not reshaped, as you see in your command r = reshape(H*reshape(x,4,[]),[],1);
, you reshaped the data itself.
In that case, you are going to add noise, then using ML estimation based on the received data. So that, H will be H without changing, what will be changed is the received data, you can reshape it similar to that way in the transmitter reshape(y,4,[])
, where y is the received data, then reshape the results again into [16 x 1].
Good luck
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