## 1 Introduction
### Related Works  
Several spectrum sensing techniques have been proposed so far, which can be classified as narrowband and wideband according to the bandwidth of the spectrum sensed. Narrowband sensing techniques are limited to detect the presence of primary signals in a single band, while wideband techniques aim at jointly or sequentially monitoring multiple bands. In what concerns narrowband sensing, energy detection (ED) [5, 6], matched filter detection (MFD) [7] and cyclostationary feature detection (CFD) [8] are widely discussed in the literature. For wideband sensing, recent studies point to three major techniques: energy detection [9, 10], wavelet-based detection (WD) [11] and compressed (or compressive) sensing detection (CSD) [12, 13]. Eigenvalue-based detection [14, 15, 16, 17] are one of the most recent and promising techniques for spectrum sensing. Likewise ED, eigenvalue detection can be applied to narrowband and to wideband signals. In wideband ED, the presence of the primary signal is detected from the energy of the received signal in each pre-defined band. The spectral partition can be done by using a filter bank approach [18] or by splitting the wideband signal into parallel narrowband signals via fast Fourier transform (FFT) [9, 10]. In this case, the FFT is applied to the time domain samples of the received signal and the presence or absence of primary signals in each band is jointly determined by the energy level of the corresponding frequency-domain signal samples. Wavelet detection uses the wavelet transform to detect discontinuities in the power spectrum density (PSD) of the signal received by a CR, thus defining the frequency boundaries in the primary signal [11]. The frequency boundaries are in local maxima of the first derivative of the wavelet transform. Having established the boundaries, the level of the PSD is estimated by the average of the signal's PSDs in each frequency band estimated via wavelet transform. Compressed sensing uses a procedure of parameter estimation from a sampling with rate below the Nyquist rate (sub-Nyquist sampling) [12, 13]. Through the solution of an optimization problem, which is typically convex, the signal's PSD is estimated and the signal presence in each band is determined using a procedure analogous to the one adopted in the wavelet transform approach. In eigenvalue-based spectrum sensing, the test statistic is computed from the eigenvalues of the received signal covariance matrix [14, 15], as for instance the eigenvalue-based generalized likelihood ratio test (GLRT), the maximum-minimum eigenvalue detection (MMED), also known as eigenvalue ratio detection (ERD), and the maximum eigenvalue detection (MED), also known as Roy's largest root test (RLRT). The advantage of eigenvalue-based spectrum sensing is that no _a priori_ knowledge about the primary transmitted signal is needed for computing the test statistic. In schemes like the GLRT and MMED, the knowledge of the thermal noise variance is not needed either. Although spectrum sensing can be performed by each CR individually and independently from others CRs, cooperative spectrum sensing is being considered as a possible solution for problems experienced by cognitive networks with a non-cooperative spectrum sensing, such as receiver uncertainty, multipath fading and correlated shadowing [2]. Cooperative spectrum sensing can be centralized, distributed or relay-assisted [2]. In centralized cooperative sensing, data collected by each cooperating CR (e.g., samples from the received signal) is sent to a fusion center (FC) through a dedicated control channel. This process is called data fusion. After the data is processed, the FC decides upon the occupation state of the channel. Centralized cooperative spectrum sensing can be executed as well from the decisions about the channel occupancy state made by each cooperating CR individually. This operation is called decision fusion, where the final decision about the channel state is accomplished through binary operations on the CR decisions.