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FFT and filter bank based spectrum sensing for WLAN signals

As wireless communication devices have increased rapidly, much attention has been paid to the spectrum resources. Cognitive Radio (CR) technology has received increasing attention as a potential approach to utilize more effectively the radio frequency bands. CRs need to dynamically and reliably determine spectral holes which could be used for secondary transmissions. In this study, wideband multichannel spectrum sensing techniques are considered, using either FFT or filter bank based spectrum analysis. We focus on detecting spectral gaps between OFDM-based WLAN signals in the 2.4 GHz ISM band. It is found out that the limited spectral purity of WLAN signals, allowed by the 802.11g specifications, significantly restricts the ability to detect possible weaker signals in the spectral gaps between WLAN channels. Improved spectral purity of the primary signals greatly enhances the sensitivity of spectrum sensing, provided that spectrally well-contained filter bank based spectrum analysis methods are used.