Basics on I/Q Data Acquisition and Processing
R&S
®
ZNL
20
User Manual 1178.5989.02 ─ 06
4.2
Basics on FFT
The I/Q Analyzer measures the power of the signal input over time. To convert the time
domain signal to a frequency spectrum, an FFT (Fast Fourier Transformation) is per-
formed which converts a vector of input values into a discrete spectrum of frequencies.
t[s]
FFT
f[Hz]
4.2.1
Window Functions
The Fourier transformation is not performed on the entire captured data in one step.
Only a limited number of samples is used to calculate an individual result. This process
is called windowing.
After sampling in the time domain, each window is multiplied with a specific window
function. Windowing helps minimize the discontinuities at the end of the measured sig-
nal interval and thus reduces the effect of spectral leakage, increasing the frequency
resolution.
Various different window functions are provided in the R&S
ZNL to suit different input
signals. Each of the window functions has specific characteristics, including some
advantages and some trade-offs. Consider these characteristics to find the optimum
solution for the measurement task.
Ignoring the window function - rectangular window
The rectangular window function is in effect not a function at all, it maintains the origi-
nal sampled data. This may be useful to minimize the required bandwidth. However, be
aware that if the window does not contain exactly one period of your signal, heavy
sidelobes may occur, which do not exist in the original signal.
Basics on FFT
