# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 1 Introduction  
It is a common fact that a large amount of spectrum resources get stuck in an extremely low utilization resulting from the fixed allocation mechanism. This apparently has become a severe headache confronting the demand for high-rate wireless communication technology and the shortage of spectrum resources. Being a hotspot in industry, Cognitive Radio (CR) is quite impressive in its ability to detect and utilize idle spectrums. The secondary user (SU) in CR may discover an idle band with the help of spectrum-sensing technology. This is a free frequency band that may, with no time-consuming interval waiting for the primary user (PU), provide an opportunistic access to the idle licensed band, thereby boosting the spectrum utilization. Nevertheless, this may fail to work when dealing with the rapid expansion of demand for wireless spectrum resources ranging from 6 kHz to 300 GHz. More spectrum access opportunities are only be available in the CR spectrum-sensing environment, which may be extended to a few GHz wideband scenarios [1]. Unlike the narrowband ones, the wideband spectrum-sensing method is able to help users find more spectrum idle opportunities, thus achieving a higher throughput on a wide frequency band. Due to the high data rate of the wideband channel, the sampling rate has been viewed as a bottleneck of wideband spectrum-sensing technology. As far as sampling rate is concerned, wideband spectrum-sensing is either based on the sub-Nyquist (lower than Nyquist sampling rate) sampling rate [2] or the Nyquist sampling rate. The former is famous for compressive sensing [3] that needs the reconstruction of sampled signals, which is of high complexity and difficult to implement or apply in engineering practice. The compressive sensing may be problematic due to its precondition that the wideband must be sparse for all signals. The latter may be obtained in different approaches,including wavelet detection [4], multiband joint detection [5] and a detection method based on the filter bank (FB) [6]. The wavelet detection is taken as alternative that works on the wavelet transform to detect the edge of the discontinuous sub-bands. Such discontinuity may be found in the spectrum in the absence of the PU or not, making it possible to determine the location of the free sections in the wideband spectrum. The multiband joint detection converts the serial wideband signals from high-speed sampling into multiple parallel narrowband signals, judging whether or not the PU signal exists for each sub-band, the key of which is the selection of detection threshold for each sub-band. The simplest way is to select the same threshold, and the joint optimization is available to select the detection threshold of each channel aiming to promote the detection results. Both of the above methods demand high sampling rate and hardware processing speed. Based on the FB, the wideband spectrum is divided into multinarrow bands, followed by sampling and processing the filtered narrowband signals. Due to the lower spectral leakage property compared with taking fast Fourier transform (FFT) at the ADC sampling rate, the polyphase filter bank (PFB) is widely applied to wideband spectrum sensing. Study in [7] presents a multiband energy detection method based on PFB and the closed-form expressions of detection probability and false alarm probability for PFBs are derived, which performs better than the conventional FFT. A two-stage wideband spectrum-sensing utilizing PFB in [8] reduces the number of binary detections when the number of occupied primary channels is small, otherwise, the number of binary detections is larger than the total number of primary channels. Multistage PFB is applied in [9] to reduce the computational complexity and improve the detection accuracy simultaneously, which needs to estimate the center frequency of the signals. Because of less delay and power requirements, Discrete Fourier Transform Filter Bank (DFTFB) is usually employed to spectrum sensing [10].