## 5 Simulation Results  
The simulation has been carried out to investigate the performance of spectrum sensing in the MCRN. The simulation consists of the sea wave movement model and the path loss model as discussed in Section 3.1 and 3.2. We assumed that there are fifteen SUs, and all of them experience additive white Gaussian noise (AWGN) with the same variance; the path loss depends on the radio environment during communication. Each SU uses the entropy-based detection scheme with the optimal number, \(N\), and the energy detector for its local observation having an average SNR \(\overline{\gamma}\). We also assumed that SUs are unaware of relevant PU information, such as the position, moving direction and velocity. The network model considered for the simulations is a general maritime wireless network, as shown in Figure 3. It covers a narrow navigation channel with the traffic separation scheme defined by the International Maritime Organization (IMO). The network consists of one westbound and one eastbound shipping lane in parallel, each with a width of \(20 km\), and it has a land station, which acts as a central entity in cooperative spectrum sensing. A similar type of network model was also considered earlier for simulations [3]. The sea states simulated in this paper are \(4\), \(6\) and \(7\), and Table 1 shows the parameters that these three sea states have. Figure 4 shows the path loss for \(2.492\) GHz for different radio environments, including land and sea environments. The sea states considered for measuring the path loss are \(4\), \(6\) and \(7\), for which wind speed, significant wave height, average wavelength and average wave period are given in Table 1. The physical distance between the transmitter and the receiver varies from zero to 20 km, and its reference distance is 1 m. Results show that path loss in the maritime environment is comparable to one in the land environment up to sea state \(4\) (Figure 4). Severe path loss is observed at sea states higher than \(4\) \begin{table}
\begin{tabular}{c c c c} \hline
**Sea State** & **4** & **6** & **7** \\ \hline \(Wind\ speed\ (knots)\) & \(18\) & \(27\) & \(37\) \\ \(Significant\ wave\ height\ (m)\) & \(1.8288\) & \(4.2672\) & \(7.62\) \\ \(Average\ wave\ length\ (m)\) & \(24.0792\) & \(56.0832\) & \(100.1268\) \\ \(Average\ wave\ period\ (s)\) & \(5\) & \(7.5\) & \(10\) \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of sea states 4, 6 and 7. Figure 3: A simulation model of the MCRN in a narrow navigation channel. Path loss almost gets doubled at sea state \(6\) in comparison with sea state \(4\). For sea state \(7\), path loss is almost five-times as compared to either sea state \(4\) or the land environment. It becomes worse at higher sea states. Figure 4: Comparison of the path loss model on land and different sea states. Figure 5: The impact of the signal-to-noise ratio (SNR) on the probability of detecting: (a) the land network; (**b**) sea state 4; (**c**) sea state 6; and (**d**) sea state 7. The probability of detection using energy detection and optimal entropy-based detection with the same number of samples was investigated to determine its sensitivity for detecting a PU's presence for a range of SNR from -10 dB to 20 dB. According to the draft IEEE 802.22 standard [26], the probability of a false alarm should be less than or equal to \(0.1\). Therefore, the decision threshold, \(\lambda_{E}\), was set to maintain \(P_{f}=10^{-1}\). Figure 5 (a) and (b) shows that the probabilities of detection at the land and sea state \(4\) are similar to each other, because both of them suffer almost the same path loss. In Figure 5 (c), when the sea state is \(6\), the probability of detection in the case of the entropy-based detector is a little higher than the energy detection, even under low SNR conditions. However, for sea state \(7\), the probability of detection in the case of the energy detector is near zero over the entire range of SNR, because of severe path loss.