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The Hermitian symmetry is used to obtain a real-valued time-domain signal. It is a special case of OFDM called discrete multitone (DMT). It exploits a property of the discrete Fourier transform (DFT), namely that the DFT of a real-valued signal has Hermitian symmetry. The motivation is usually the channel: if the signal shall be transmitted over a low-pass ...


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There are many intepretation of OFDM to answer your question. One way I find intuitive is looking at the resolution of (Discrete) Fourier Transform. By capturing a signal during time $T$, and by assuming periodicity outside $T$, DTFT is able to provide a frequency resolution $1/T$. Thus if you choose a subcarrier spacing $\Delta f$, you will want a ...


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I understand that you receive A, B, and C and you want to separate it into A1, A2; B1, B2; C1, C2. From this, you can do the following: Model your expected signal A1 as some pulse shape (here, I assume its a tanh function, which is close to your example). Then, normally you can perform matched filtering on the received signal to find peaks at the position ...


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[EDIT: some code made available] The notion of $M$-band wavelet transform, $M\ge2$, generalizes the standard $2$-band wavelet. The theory is provides, for instance, in Theory Of Regular M-Band Wavelet Bases, P. Steffen et al., 1993. Remember that with $2$-band wavelets, one cannot obtain wavelets that are real-valued, symmetric, orthogonal and with finite ...


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Quasi-static is almost-static. In other words, for a block (or window) period of time, you could assume that your channel is static. Below, i attach a figure that depicts this scenario. As you can see the channel could be assumed static for around 100 ms.


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Quasi-static channels can be said as "block-wise" time (in)variant. For example, your channel has no variations over time (not in delay domain) for some time-period, e.g., 1 ms, but the channel may change after 1 ms.


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There are papers describing a method called multichannel cross-correlation, that recursively estimates time delay of each signal. However, If I have not missunderstood you problem you can also select one of your signal to be your reference signal and then repeatedly calculate cross-correlation against that compared to the other signals. that way you can ...


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A binary code is a relatively abstract representation of some quantized information on two levels. As such (being an abstract think), it often requires to be interpreted to turn into a real-word representation. The unitless 0 and 1 shall be concerted into quantized with dimensions, using: ancillary data, like contained in the header of the PNG image file (...


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Your image is binary or two-tone per color channel. Thus you have 2x2x2=8 possible tones, or 3 bits. In a bitmapped image, you can encode each pixel using any number of bits, then use a global table to define what color should be mapped to each code. Edit: Your code could be written as: [X,map]= imread('lena.png'); X1 = round(X/255); Each channel can ...


1

The equation you wrote down describes the case for a narrowband (flat fading) MIMO system. Narrowband/flat fading describes the scenario when your signal bandwidth is small relative to the channel bandwidth (inverse delay spread). Whatever system you are modeling, you'd have an idea of the delay spread and signal bandwidth, and could use that as a ...


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The equation $y = hs + n$ is a discrete-time model of a certain kind of wireless communications system. $s$ is a complex number, drawn from a finite set called a "constellation", that represents the information being transmitted. $n$ is a complex, circularly-symmetric Gaussian random variable with zero mean and variance usually denoted by $N_0$. $h$ ...


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One of the most readable (imho) authors on the communication systems is Simon Haykin and the following book from him would probably address most of the issues you would encounter in a wireless communication system analysis. Modern Wireless Communication Systems Similar known authors do have related books, but I guess most compact academic begining would be ...


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The station here is continuously monitoring the energy in the medium. Let us say you want to transmit in channel 1 of 2.4GHz band. Your RF hardware is always in receive mode if you don't want to transmit anything. You are continuously sampling the incoming signal in this particular channel. You use ADC to sample and compute the energy $E = \sum |x[n]|^2 $ of ...


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I don't know why we can't use RRC filter with OFDM ?!! Yes, of course, we do can. The only disadvantage for that is the complexity. however the gain will be in spectral efficiency improvement and bandwidth efficiency because of avoiding the needs of guard interval which is used in the regular OFDM. In Engineering, we should always ask, why and why not? why ...


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This sounds like the classical job of a polyphase filterbank, and a cascade of polyphase synthesizers. Yes, do this in complex baseband. First, you use your filterbank to divide the 25 MHz in 25 kHz wide channels. Then, you use polyphase synthesizers to combine subsets these channels back to the channel widths you need, and then you use another one to ...


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The mistake could be in assuming you know which angle $a$ to use when you don't know if the source is in front of or behind the array yet. In other words, you need to solve the same problem for $a$ that you're solving for $b$. For your approach to work, you need to make sure side $a$ is the closest side to the source. You could find the closest side by ...


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The beauty of PCM (pulse-coded modulation, i.e. time samples of audio) is that the mathematical sum of two signals sounds like the acoustical superposition. So, just add your two sines sample-wise.


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Let's say we have a function that we transmit which is: $$x(t)$$ Transmitting the function is kinda easy part. We assume there is just the function we are trying to send. However things are a little bit ugly in the air. Let's look at the receiver part. What we generally consider first is the AWGN. Which is: $$r(t) = y(t) + n(t)$$ $r(t)$ is our received ...


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By doing PCA, the principal components you will get will not correspond to a single recording, but rather to a mix between them. PCA is a feature extraction method, whereas what you are looking for, seems to me, as a feature selection problem. Also, if you have so many simultaneous recordings, all affected by the same sources of noise, why not perform ...


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I can't comment because I'm a new user... have a look to these questions: Estimating the time offset (delay) between two audio signals in real-time Generalized Cross Correlation Time delay estimation of oscilloscope signals using cross correlation


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One approach would be to use the spectral flux of a sequence of STFT frames $X(n,k)$, namely $$ \mathit{SF}(n) = \sum_{k = -N/2}^{N/2 - 1}{H(\left|X(n,k)\right| - \left|X(n-1,k)\right|)}, $$ where $n$ is the frame number, $N$ is the size (in samples) of each frame, and $H(x) = \frac{x + \left|x\right|}{2}$ is the half-wave rectifier function. Intuitively, ...


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Hermitian Symmetry is used when you want the IFFT output to be real-valued, so that it could be transmitted directly over a wire, for example. 2-3. At the input to the N-point IFFT, you should have f(N-n) = conj(f(n)) where n = 0 to N/2.


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