Question: I have a scenario where I have two OQPSK modulated signals and I need to increase the power of one signal by 3 dBm while keeping the other signal at the same power level. Could someone guide me on how to achieve this in Matlab.

Answer: 
"**Is there a way to measure frequency (detect pitch) better than FFT, that is, with better resolution in less acquisition time?**"

yes there is. or are. there are multiple better ways to do musical pitch detection in real time that are far, far better than running an FFT.

consider **:** 

Average Magnitude Difference Function (AMDF)

$$ Q\_x[k] = \sum\_n |x[n] - x[n-k]| $$

Average Squared Difference Function (ASDF)

$$ Q\_x[k] = \sum\_n (x[n] - x[n-k])^2 $$

Autocorrelation Function (AF)

$$ R\_x[k] = \sum\_n x[n] x[n-k] $$

note that i am playing fast-and-loose with the limits to the summation.

note also there there are no assumptions made about waveform shape or zero-crossings or other threshold crossings. the only assumption is that when the lag $k$ is approximately a period (or two periods or some other integer multiple of the period length), $x[n]$ looks a lot like $x[n-k]$. so the only assumption is that pitch is related to fundamental frequency of a periodic or nearly periodic (what i like to call "quasi-periodic") function.

my favorite is ASDF (and that is a thinly-veiled trade secret i just announced to everyone, but folks on *comp.dsp* knew that already). these are all time domain, AMDF and ASDF look very similar and ASDF looks like an upside-down version of AF. you are looking for nulls in AMDF or ASDF or peaks in AF which would correspond to potential period lengths of the quasi-periodic input.

here are a couple other tricks:

1. you can always correlate the *most current* $N$ samples against some $N$ samples delayed by $k$. that way you are dealing with the most current data possible in the real-time application.
2. you need not calculate the correlation for every integer lag $k$. in fact, since you like log-frequency, the spacing for larger $k$ might be bigger than the spacing for smaller $k$.
3. when a potential null (AM\_F) or peak (AF) is found, you can compute the correlation for adjacent integer values of $k$.
4. between adjacent integer values of $k$, you can do interpolation to determine the peak location to a fractional-sample precision. i won't tell you how. use your imagination.
5. the whole trick (and this is the secret sauce where trade secrets and IVL patents apply) is to choose the **correct** peak or null when there are multiple candidates. choosing the incorrect peak or null will result in an "octave error". i'm not gonna tell you how to do that. use your imagination.

send me an email and we can discuss contracting terms if you want me to design you a kick-ass pitch detector. much better than YIN, which, in my opinion, works like shit.
I already answered your question here: <https://stackoverflow.com/questions/33667275/fast-frequency-measurement/33678202#33678202>

But, in summary, in certain circumstances, you can interpolate an FFT result to finer resolution that FFT bin spacing, thus allowing you to use a shorter data window for better time resolution.

But FFT frequency is not pitch frequency. And for some music instruments (those that produce slightly inharmonic overtones), neither is the auto-correlation function (or its relatives such as AMDF) frequency. That's because pitch is a psychoacoustic phenomena.