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The x-axis in the second diagram refers to time (in the unit of OFDM symbols), and the y-axis is the subcarrier index. If a user is assigned a set of squares in the second diagram, it means that that user can transmit on those subcarriers in that symbol. As an example, let user 1 be assigned to...
I am not sure what you are asking here. Power is something you measure across frequency, or across time; energy of the signal is across frequency/time. As far as the cos function goes, if you integrate a dirac delta function, the integral is one, not infinity.
Not sure I understand your question, but MATLAB does have the right color-coding for .c files. You need to run anything you would run in C though by first making a new C file and then editing it, then compiling it into a mex file and then running it. the extension on mex files is .mex, which is...
You can do this with 4 states: even #0's/even #1's, even #0's/odd #1's, odd #0's/even #1's, and odd #0's/odd #1's. From there you can figure out the state transitions and outputs.
This is straightforward. If you load the data into the variables "I" and "Q", just type "plot(complex(I,Q))" to get the constellation diagram. In Excel, just do some scatter plot with your x-axis as I and your y-axis as Q, as you said, and you should see it there as well.
You need to:
1. generate random bits and store them
2. modulate them (QPSK)
3. do whatever transmit MIMO processing you have
4. send them across a channel
5. demodulate them (QPSK demod)
6. compare your demodulated bits to transmit bits; BER from monte carlo is given by...
Complex AWGN affects both amplitude and phase/frequency. Remember that when you add complex Gaussian noise, the value you add is, in fact, complex; thus, both values will change.
If, for example, you added only real AWGN, you would see only the real component in the signal change, so the...
This is not correct... any two orthogonal vectors in the plane that is orthogonal to the vector will suffice. And you can use Gram-Schmidt; just pick any two random vectors and apply the Gram-Schmidt process to get 3 mutually orthogonal vectors.
Why are you looking for mathematical literature on this topic? Standards specify the pilots, and they are used for estimation. I doubt there are many textbooks that include channel error estimation from pilots unless they are standards books.
If you want to find more information on channel...
I am not sure I understand the passband part exactly, but generally how OFDM works is this: you take the time domain signal and then do some sort of pulse shaping (say, root-raised cosine with a rolloff factor of 1/2). I am not sure what kind of pulse shaping occurs in OFDM signals in real...
Not sure what pilots are used for in analog communication, but they are regularly used in digital communication for the purposes of channel estimation, timing/frequency offset estimation, sampling clock offset estimation, etc. A pilot signal in digital communication is a known value at both the...
You can use the Gram-Schmidt method:
https://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process
Basically, select any 2 random vectors and apply the Gram-Schmidt process to get a set of 3 orthogonal vectors. There is probably an easier method, but that one is straightforward from what I can...
I do not think a Kalman filter would be appropriate for this problem; I have not worked with them much, but doing a quick reading on them says that they are mostly used for updating an estimate over time, which isn't really your goal here. Offhand, I think a cross-correlator with a threshold...
I think matched filters are mainly used for ease of analysis; they make more sense from a filtering/convolutional perspective. Because both yield the same result and both have the same computational complexity, there is not much in implementation/performance that changes between the two.
Also...
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