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3

How can I manipulate/control $R_S$? You usually start with a desired pulse rate $R_S$. Then, the number of samples per symbol is $f_sT_S$, where $f_s$ is the sampling rate and $T_S = 1/R_S$. The resulting signal bandwidth will be $B = (1+\beta)R_S/2$, where $\beta$ is the excess bandwidth in the pulse you choose to use. In other cases you have a desired ...


2

In the case of BPSK, the data rate equals the symbol rate (one bit per symbol). One way to control $R_S$ is to change the modulation. For example, you can increase the number of bits per symbol by choosing a higher order modulation like 16-QAM which has 4 bits per symbol. There is nothing wrong with your calculation. The plot is a single sided spectrum so ...


0

Of course, if you have a formula describing the system's response to an arbitrary input $x[n]$, you will obtain the response to an impulse if you choose $x[n]=\delta[n]$. However, I suppose that the idea of the exercise is to obtain a closed-form expression for the impulse response. You should recognize that the given input-output relation is just a ...


0

This answer is a bit late, but the problem has quite an elegant solution based on the Fourier transform and I wanted to add it. The action of the BSC can be modeled as a$\pmod 2$ sum of the data bit, $X$, and a random noise bit, $N$, who's probability distribution is $P(N=0)=1-p$ and $P(N=1)=p$, $$Y = X \oplus N.$$ The distribution over the output, $Y$, ...


-1

That depends on the length of the cyclic prefix compared to the FFT, and of course on the multipath propagation. 10 dB sounds harsh, (so, probably an implementation error) but yeah, CP-OFDM is quite obviously cleverer than Zero-padding, and every OFDM introduction explains why (I'm a bit surprised this question comes now – you've been working with OFDM for ...


0

I'll assume that YOU assume you have perfect channel knowledge so that when you simplify your equation you get to (using the paper's notation): $ \mathbf{y} = \mathbf{D}\mathbf{s} + \mathbf{U}^H\mathbf{n}$ You have a signal part and a noise part. So the signal power will have a gain which is given by the diagonal entries of $\mathbf{D}$. Each noise ...


1

It is important to understand the what the channel matrix $\mathbf{H}$ physically means. The channel matrix is a symmetric matrix that contains the fading coefficients, that is, in row $i$ column $j$ the value ${h}_{ij}$ is the coefficient between the $i^{th}$ transmit antenna and the $j^{th}$ receive antenna. The gain between any transmit/receive antenna ...


0

The paper actually deals with $\pi/4$-QPSK, not with plain DQPSK. In $\pi/4$-QPSK, the symbols are always rotated by a minimum of $\pm \pi/4$. Rappaport's Wireless Communications textbook has this table: \begin{align*} 11 \rightarrow & \, \pi/4 \\ 01 \rightarrow & \, 3\pi/4 \\ 00 \rightarrow & \, -3\pi/4 \\ 10 \rightarrow & \, -\pi/4 \end{...


0

Usually (almost always), simulation is done using the discrete time baseband signal. Lets say you want to simulate 32 BPSK symbols (either +1 or -1). The MATLAB code for this is: symbols = sign( rand(32, 1) - 0.5 ); Now that you have the symbols, the next step is to upsample. You can either use the MATLAB function or do it yourself: symbols_up = ...


1

What do you mean under encryption-grade randomness? It is not recommended to use linear random generator in most of the encryption cases. PN-Sequence is a mutation of the LFSR. Because of the linearity, all future values can be calculated easily. So be careful using linear pseudo random generators.


3

Quantization and encoding are largely independent. "Symbols" is another word for "pulses", and the line encoding can also play a role in how the information is transmitted. Say you quantize one sample to $256 = 2^8$ levels, or 8 bits/sample. In order to transmit those 8 bits, you can, among other options: use binary encoding, which requires transmitting ...


0

In theory you need to recompute your precoding for every subcarrier. In practice you might not want to do this as the computational complexity is pretty high. What one then typically does is grouping adjacent subcarriers and time indices together into "chunks" (I believe they are called resource blocks in LTE?) and just compute one precoding strategy for ...


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