## 6 Results and Discussion
### Analysis of the Simulation Results
Figure 9 shows the results of comparing the proposed algorithm with two algorithms which both involve single users but involve a different number of thresholds. In general, at low signal-to-noise ratios, the difference in power between the signal and the noise is small, resulting in far worse detection than double-threshold energy detection algorithms. As shown in Figure 9, in the signal-to-noise environment simulated in this paper, the double-threshold detection algorithm is overall better than the single-threshold detection algorithm. This is because the traditional single-threshold detection algorithm is more prone to false detection if there is burst noise or other interference, resulting in the user not being able to accurately use the idle bandwidth. The algorithm in this paper uses a centralized collaborative detection algorithm, and the fusion center can use different fusion judgment rules. Thus, simulation experiments are conducted for the collaborative detection algorithm based on different judgment rules. Under the condition that the probability of a missed detection and the probability of a false alarm are constant, three users are involved in collaborative detection. The following algorithms are simulated and compared: (1) Double-threshold OR criterion collaborative detection: As only one of the three users determines that the band is vacant,  
Figure 11: The simulation results. Figure 10: The algorithm simulation experiment results. the final detection result is that the band is not used. (2) Double-threshold AND criterion co-operative detection algorithm: All three users are judged to be band-occupied, and then the final judgment is that they are in a state in which the band is occupied by the primary user. The third algorithm is (3) the algorithm proposed in this paper. The simulation results are shown in Figure 10.  
From Figure 10, it can be seen that the proposed algorithm in this paper already has detection results in an environment with a signal-to-noise ratio of 11 dB, and the detection probability is up to 0.986. The AND criterion collaborative detection algorithm requires the same detection results for all users, so the possibility of generating false detections is greater than that for the other two collaborative algorithms. However, the OR criterion collaborative detection algorithm is less affected by the environment in which the user is located; it can also be seen from Figure 10 that the detection results are much less effective than those of the other two algorithms. Different decision rules can achieve different effects in collaborative detection, and the OR criterion has a better effect. On this basis, the algorithm proposed in this paper improves the detection performance. Because k represents the number of sensing users in the k-fusion adjudication criterion, the principle is that the idle band is detected by all k users before the fusion center determines that idle bands exist. k is adjustable, so collaborative detection algorithms using k-criteria fusion judgments can have different detection effects. This simulation experiment lists three collaborative detection algorithms using k-criteria fusion judgments for various numbers of users, with the number of users being two, three, and four, respectively. The simulations of the proposed algorithm and these three algorithms are compared as follows. Figure 11 shows the simulation results. As can be seen from the figure, although the k-fusion judgment criterion can be used to find the optimal effect by adjusting the parameters, the process is long and time-consuming, especially in the case of many perceptual users. The more k values there are that can be chosen, the more possible effects there are, and the greater the difficulty of tuning the parameters.