# Unipolar Transmission Spectrum

I am currently trying to understand mathematically the amplitude shift keying modulation technique. My question is, say I wanted to compute the Fourier series of a basic pulse train, it is quite simple since it is periodic. But in digital transmission (Unipolar) the signal is random, how does one compute the spectrum of this transmission. I hope my question makes sense.

Here is a snapshot of some notes I found online. The data signal is a random signal of 1's and 0's. How can anybody interpret the spectrum of it? Thanks.

The spectrum of a pulse train with a defined pulse with T that is indeed random in duration of zeros and ones would have the magnitude envelope of a Sinc function (PSD with envelope Sinc^2) with first null at 1/T. Since the data is random the spectrum will be continuous. Similar to this is something like GPS where we transmit Pseudo-random patterns, but the pattern repeats (assuming no data modulation which again adds randomness). In this case, whenever there is a repeating pattern, you would see discrete tones at the repetition rate and every harmonic of the repetition rate, and the envelope would still be the Sinc function as described with nulls at 1/T. (Thus with doing forensics on such a spectrum you can discern the length and repetition rate of the sequence based on the spacing of the spectral lines, and the number of "chips" in the sequence based on the location of the null, etc.).

Finally multiplying by the carrier just moves this spectrum (which is best viewed in my opinion as a positive and negative spectrum even at baseband, as then you can translate it from there to any frequency with no changes to its features). The fact that this is on-off keying means that it will have a DC component, so the carrier will come through very strong at the RF output. In comparsion if the signal was bipodal (which would then be BPSK modulation), with an equi-probable stream of 1's and 0's you would have no DC at baseband (avg =0) leading to supressed carrier at RF. The carrier power in the former case is wasted (no information added) so is less efficient in terms of BER vs power than the latter case.

The reason for this is the base square pulse has a FT that is the Sinc function, and your random data is represented as a stream of impulses (with random positive and negative sign) that is convolved in time with your pulse (effectively a zero order hold). If the impulses are random in a pattern that never repeats, the spectrum of that pulse stream will be a continuous flat spectrum: white noise. Therefore in frequency you multiply white noise with the Sinc to get the spectrum before being upconverted. The mixer translates this spectrum to the carrier.

Your picture just shows the main lobe, but if your pulse is indeed rectangular, you will have all the sidelobes of the Sinc function out to the effective BW (based on the rise/ fall time of your pulse).

I hope this clears it up for you!

• I quote "If the impulses are random in a pattern that never repeats, the spectrum of that pulse stream will be a continuous flat spectrum: white noise". Does this mean the spectrum information is useless? Also If some random transmission is segregated into pulses and described using the linearity property: x1(t)+x2(t) = X1(jw) + X2(jw) Also keeping in mind that the other pulses are just shifted in time. Would this mean that the spectrum is a summation of sinc functions? Is that why the spectrum is white noise? I understood your reply, this is just another speculation of mine. Commented Mar 2, 2017 at 1:23
• No not all - it would have maximum information content! I can send you data that is meaningful but completely uncorrelated to precious data I sent. (Further consider data compression algorithms: If I optimally compress the data, it will be completely white noise (as if any patterns remained it means I could theoretically compress the data further). In practicality patterns will exist, and these will manifest themselves as spurs in the spectrum, but what I described as a Sinc function envelope with nearly continuous spectral content I have seen many times. Commented Mar 2, 2017 at 1:27
• Yes good speculation, I believe you are correct, each pulse would result in a sinc of differing phase. Note that some pulses would be wider by integer amounts, and therefore their own Sinc is narrower. That and the varying phase contributes to the randomness of the result (more specifically any lack of pattern repetition; as soon as you repeat in time you create discreteness in frequency). Commented Mar 2, 2017 at 2:43
• Yes!! Thank you for confirming my understanding of this matter. It is sometimes hard to interpret and visualize the frequency domain. Thank you for your informative responses. Commented Mar 3, 2017 at 2:20