FFT stands for “fast Fourier transform” and it is used to measure DFT or “discrete Fourier transform” that basically allows the conversion of time to a certain RF or radio frequency for example.  FFT represents a set of rules or mathematical guidelines in terms of DFT measurement which can involve time or space conversion to frequency or the reverse formula involving frequency converted back to time.  As an algorithm, FFT can be applied in various fields including mathematics, and the fields of engineering and science.  When involving FFT with a duty cycle, the measurement is based on the percentage of time that it takes a signal or frequency to reach above 20 decibels beyond what is considered standard for the noise floor.  Anytime the radio frequency for example reaches a level above 20 decibels, then the FFT duty cycle may be computed using the set of algorithmic rules provided for by FFT and mathematical standards.

In the case of searching for the right channels or frequencies using mobile gadgets and devices, the FFT duty cycle may be interpreted as the actual time of channel usage or utilization.  Whenever the signal goes beyond the 20 decibel mark, computer software will detect the presence of the signal and the corresponding time it appears.  Using this time as the main variable, channel utilization can then be computed in terms of FFT duty cycle.  Through software that is able to compute and present FFT data cycles, signals or frequencies that are measured may be represented with different indicators.  Common software will display the real-time FFT and the corresponding FFT duty cycle.  Real-time FFT refers to the signal power or strength while the FFT duty cycle will represent the time it took for the signal to go beyond the benchmark of 20 decibels.

Other spectrum analyzing software may also display FFT duty cycles at certain frequency levels or channels.  In the case of the 802.11 channel, the FFT duty cycle represents the time percentage wherein an interference is detected.