## 8 Future Challenges for the Enhancement of ED Limitations
### Research Challenges Related to the Different SS Methods  
Among different narrowband methods for local SS, the ED is just one of the non-cooperative methods (Table 4). To improve the sensibility and reliability of the available spectrum detection, thus far in the relevant literature a number of distinct narrowband SS approaches have been proposed. The comparison of the main performance parameters among ED and other relevant non-cooperative local SS methods is presented in Table 5.  
\begin{table}
\begin{tabular}{p{108.4 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Parameters for Comparison with ED Method** & **Matched Filter Detection (MFD)** & **Cydostationary Feature Detection (CFD)** & **Entropy Detection Method (END)** & **Waveform Based Detection (WBD)** & **Goodness of Fit Test Detection (GFTD)** & **Eigenvalue Based Detection (EDD)** \\ \cline{2-7}  & [7,83, 84, 85, 29, 132, 133] & [77,84, 86, 89, 90, 91, 104] & [85, 94, 95, 9  
Regarding the accuracy of detection at different SNRs, the ED method is less accurate compared to the cyclostationary feature detection (CFD), entropy detection (END), goodness of fit test detection (GFTD) and eigenvalue based detection (EBD) methods (Table 5). Additionally, for the challenging case of the detection of signals at low SNR, matched filter detection (MFD) and waveform based detection (WBD) have significantly better detection accuracy than ED (Table 5). In the case of WBD and MDF methods, this is due to the necessity of obtaining a priori knowledge of some of the PU signal parameters. Therefore, the amount of information received from PU needed for performing accurate SS significantly differs among the local SS methods compared in Table 5. While END, GFD and EBD do not require any a priori information about the PU signal, the aforementioned MFD, WBD and CFD techniques request precise information about the PU signal pattern. This information might include details such as the transmitter pilots, preamble, guard bands, etc., while WBD even requests the synchronisation between the PU and SU. However, accurate prior information about the PU signal may not be achievable at any time, since SU and PU do not exchange information constantly. This represents the main limitation of the MFD, CFD, and WBD techniques. Nevertheless, for the MFD and WBD methods, it has been shown that the accurate prior information of the PU signal contributes to a reduction of sensing frequency and has an equal or even better detection performance for lower sample quantity when compared to other local sensing methods (Table 5). Compared with the ED method, the CFD, END and EBD methods request a similar sample quantity for accurate SS. This is significantly larger than for the MFD and WBD methods when equal detection accuracy has to be accomplished. This is an important disadvantage which limits the practical implementation of these methods in CWSNs, since a higher sample quantity and more frequent sampling periods contribute to the faster depletion of wireless sensor batteries. Compared with the ED method, the MFD, END, WBD and EBD methods are more robust against NU while the GFTD technique has a similar level of robustness (Table 5). However, CFD is considerably more resistant against NU than ED and the other methods, due to the fact that the noise is typically not cyclostationary. The more accurate PU signal detection in the channels impacted by the higher NU variation can be accomplished by exploiting the cyclostationarity effect of the PU signal in the case of the CFD method. In the case of the EBD method, more accurate PU detection is observed for better correlation structure and diversities in the eigenvalues of the statistical covariance matrix of noise and signal. However, in order to accomplish this in practice, the computational complexity of the CFD and EBD methods is significantly higher than the ED and other SS methods (Table 5).