In today's world, the proliferation of wireless devices grows in an exponential form, demanding more and more data communication capacity over wireless links, which leads to a scarcity in electromagnetic spectrum availability. Nevertheless, spectral occupancy studies have shown a low utilization rate of some licensed frequency bands both in time and geographic location. Cognitive Radio (CR) is a promising technology to address spectrum sharing by selectively detecting bands not being in use by a Primary User (PU) and opportunistically allocating them for a Secondary User (SU) utilization. However, the detection of unused frequency bands, which is the most challenging problem in CR, can be overcome by exploiting cyclostationary features exhibited in the received signal. Most, if not all, communications signals have cyclostationary signatures, which can be used to detect the PU. However, detecting these signatures requires a fine resolution in cycle frequency, which can cause high computational complexity when computing the discrete Spectral Correlation Function (SCF). This paper presents an innovative adaptation of the well-known FFT Accumulation Method (FAM) to efficiently obtain the SCF in a zoomed/local sub-band of the entire frequency and cycle frequency (f;α) plane. Computational complexity comparison of the FAM and the proposed zoom FAM (zFAM) algorithms is addressed, and computer simulation results are presented to illustrate the zFAM superiority to detect the cyclostationary features in a small region of the (f;α) plane.
- Cognitive radio
- Cyclostationary detection
- FFT accumulation method (FAM)
- Spectral correlation function (SCF)
- Spectrum sensing