# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 6 Results and Discussions
Figure 8 shows the total carried traffic when PU traffic load \(\rho_{1}\) varies from \(\lambda_{1}\) = 0.0006 to 0.006 under both \(\mu_{1}\) = 0.006. Meanwhile, CU traffic load \(\rho_{2}\) is kept constant at 0.01, 0.05, 0.1, and 0.2 with \(\lambda_{2}\) = 0.2. Figure 8 also shows the variation among the total carried traffic according to different CU traffic load (\(\rho_{2}\)) at 0.01, 0.05, 0.1, and 0.2. In this example, the total carried traffic also increases when CU traffic load increases. The CU channel allocation failure under the heavy CU traffic load often makes the CU calls queued more frequently. This may be explained as follows: if CU traffic enters into a few channels that the PU spectrum band leaves for the overloaded CU traffic, the continuous CU call attempts can make the queue grow. At this point, an arriving PU request makes \(m\) CU calls preempted as PU traffic increases, and this makes the CU queue size increase. In this situation, if the service time of the PU call becomes longer, this builds up a significant queue size. In Figure 8, however, we can see that the total carried traffic is slightly reduced as PU traffic load increases. This means that no CU calls can overflow over the licensed spectrum since the licensed spectrum channels allocated for PUs are quickly occupied under heavy PU traffic. That is, when the CU traffic load is switched to a normal situation, the queue size is continuously reduced and approaches its steady-state level. This phenomenon depends on the service time ratio, cutoff values, and the ratio of the spectrum bandwidth required for each type of traffic. Figure 8: Total carried traffic for both PU and CU calls. Figure 7: Mean dwell time for CU calls. Figure 9 shows the combined service quality factors when the PU traffic load \(\rho_{1}\) varies from \(\lambda_{1}\) = 0.0006 to 0.006 under \(\mu_{1}\) = 0.006 and 0.008, respectively. Meanwhile, the CU traffic load \(\rho_{2}\) is kept constant at 0.01 and 0.1 with \(\lambda_{2}\) = 0.2 and weight factor \(W_{GT}\) = 3.0. Figure 9 also shows that the combined service quality factor (CQF) increases at nearly the same rate as \(\lambda_{1}\) increases to 0.006. In this example, the CU arrival rate (\(\lambda_{2}\)) is fixed as 0.2. That is, the lower the CU service rate (\(\mu_{2}\)) is, the higher the CU traffic load (\(\rho_{2}\)) is. Hence, the higher the CU traffic load is, the better the CQF is. In Figure 9, it is shown that the CQF when \(\rho_{2}\) is 0.1 is better than the CQF when \(\rho_{2}\) is 0.01. Meanwhile, as shown in Figure 6, the higher the PU service rate \(\mu_{1}\) is, the lower the blocking probability of the PU call requests is. That is, the higher the service rate (\(\mu_{1}\)) is, the better the CQF is. In Figure 9, however, we can see that the CQF when \(\mu_{1}\) is 0.006 is better than the CQF when \(\mu_{1}\) is 0.008. This can be explained as follows: the lower \(\mu_{1}\) is, the longer the PU spectrum channel is occupied. In this situation, the rate that CU traffic overflows into the PU spectrum channel and then is preempted again, becomes lower than the situation with the higher \(\mu_{1}\). This makes CU calls queued more frequently due to preemption and delayed more due to this queueing. Consequently, the CQF becomes lower in the higher service rate (\(\mu_{1}\)).