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Spreading refers to the process of multiplying your data sequence with a higher rate PRN sequence often but not necessary such that one complete duration of a repeating sequence occupies the time duration of one data symbol. For example one such modulation using BPSK to send a “1” you send the entire sequence before it repeats and to send a “0” you send the ...


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Plenty! In fact, since the mid-1970s, people in security have been working on automatic transmission classification. It's a hard problem, and you logically can't detect all transmission schemes, but it's something that's being sold as service and as product, especially for government (and three-letter government agencies). These guys, for example, focus on ...


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You are right. If you have MATLAB, you can do a little experiment to double check yourself: h = randn(100, 1); % make some random impulse response F = dftmtx(length(h)); % make the DFT matrix (100-by-100) abs(fft(h) - F*h) % this number should be small


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Usually an IQ signal is generated from a real (RF, AF, etc.) signal by a radio (by quadrature heterodyne, Tayloe demodulation, complex vector arithmetic, or otherwise). However, if you need IQ data, and the radio only generates I (or real) component samples, a Hilbert transform can be used to generate “fake” Q components (an approximation by making some ...


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The transmit power for a single output transmitter typically refers to the output of the transmitter power amplifier that is connected to the transmit antenna, and that power is almost entirely radiated over the air. It is power in Watts. The power needed by the transmitter includes this and everything else to run the transmitter. The power amplifier (PA) ...


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A Pseudo-random noise (PRN) sequence ia a closer approximation to white random noise in that its energy is spread equally over the occupied frequency band (The energy is spread as a Sinc function if reconstructed with pulses just because of the pulse shape but the underlying code as a stream of impulses has a more uniform distribution), and its auto-...


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Below 1D argumentation also explains the 2D case. First consider the DTFT property for the pair $x[n] \longleftrightarrow X(e^{j\omega})$ $$ e^{j\omega_0 n} \cdot x[n] \longleftrightarrow X(e^{j(\omega - \omega_0)}) $$ Then recognise that $(-1)^n = e^{j \pi n} $ which yields: $$ e^{j\pi n} \cdot x[n] \longleftrightarrow X(e^{j(\omega - \pi)}) $$ The ...


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try decoding the signal using various modulation schemes and see which gives you the most confidence well, simply because your "confidence" measure is basically equivalently hard to design generally enough as the other methods you mention. Also, the search space is simply too humongous. How would you decode all possible combinations of constellations, ...


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Assuming the OP wants to manipulate a time domain function such that zeros are inserted in the frequency domain result, here is a simple approach: Simply replicate the time domain samples and then divide by the number of repetitions to normalize and this will result in the same frequency domain result as the series that was replicated, with additional zeros ...


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There are many applications for the analog equivalent of the Hilbert Transform, often implemented with “90 degree power dividers” also referred to as “quadrature splitters” as well as broadband variants implemented with phase tracking networks. One application is single sideband frequency translators done with IQ mixers; I have another posting that details ...


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Yes and no. The original ICA methods were not made for the case where there is noise on the signal. However, there exist relatively straightforward extensions that work for noisy signals (e.g. [*]). In this case, you can expect some degree of noise suppression but it won't be perfect. That means that ideally, your desired signal components will stick out ...


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You need to distinguish between transmitter (TX) and receiver (RX) here. For the receiver it is generally quite easy to gain knowledge of the channel: have the transmitter send a few known symbols, then the receiver can estimate the channel coefficients (e.g., via Least Squares or MMSE). The receiver needs this channel estimate for equalization. For example, ...


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