## 6 Results and Discussion
### Simulation Results  
Figures 6 and 7 show the sensing performance during HD and FD sensing slots, respectively, in term of the probabilities of finding the non-availability of spatial-temporal spectral holes, and the probabilities of missing the availability of spatial-temporal spectral holes. There was no self-interference signal during HD sensing slots. However, the sensed signal during FD sensing slots was corrupted with the self-interference signal as FD-SU TX was transmitting simultaneously. The results indicate the existence of different spatial and temporal spectral opportunities for FD-SU TXs present at different spatial positions in CR-IoT spectrum-heterogeneous environment. It can be observed that the probabilities of missing the availability of spatial-temporal spectral holes during HD and FD sensing slots (\(P_{MiST}^{0}\), and \(P_{MiST}^{1}\)) was always greater for FD-SU TXs in \(R2\) than those in \(R1\). This is because the \(P_{MiST}^{0}\), and \(P_{MiST}^{1}\) for FD-SU TX in \(R2\) involves spatial (excessive) false alarms, determined by \(ON\) state of the PU. In detail, temporal false alarms for FD-SU TXs (\(R1\) and \(R2\)) arose because of the miss-detection of the absence of a PU. The spatial false alarms for FD-SU TXs in \(R2\) arose because of the detection of presence of a PU despite the fact that the FD-SU TX is outside the transmission region. The spatial and temporal false alarms depend on the usage of channel by the PU, and number of sensing samples, respectively. It can be observed that the reduced use of channel by the PU and a greater number of sensing samples result in fewer spatial and temporal false alarms, respectively, and results in the improved spatial-temporal spectral hole-sensing performance. The influence of different INRs in terms of SIS factors (\(X\)) over the spatial-temporal  
spectral hole-sensing performance is shown in Figure 7. The temporal false alarms increased with \(X\), which is intuitive because \(X\) increases the interference power. Thus, our results illustrate the requirement for identifying the optimal range of the secondary transmit power to obtain the desired spatial-temporal spectral hole-sensing performance. Figure 6: Sensing performance of spatial–temporal spectral holes during the half duplex (HD) sensing slots. Figure 7: Sensing performance of spatial–temporal spectral holes during the full duplex (FD) sensing slots. Figures 8 and 9 illustrate the performance of the UoS scheme, and show the average number of sensing slots used for the successful secondary communication (in each time-slotted frame). The performance of the UoS scheme was investigated for different PU active state probabilities (\(P_{ON}\)), SIS factors, and QoS constraints (\(P_{F.nST}^{0^{-}}\), and \(P_{F.nST}^{1^{-}}\)). In Figures 8 and 9, the QoS constraints (during HD and FD sensing-slots) were set to 0.9, and 0.7, respectively. Reduced QoS constraints means less protection to the PU and more spectral opportunities for FD-SU TXs (\(R1\) and \(R2\)), and hence, average number of sensing slots used for the successful secondary communication increases. In detail, the results show the reduction in the average number of successful secondary communicating sensing slots with the usage of channel by the PU node, considering a fixed PU mean \(OFF\) duration (\(R\)). The reason is that the spectral opportunities (spatial and temporal) decrease under the considered scenario. The average number of successful secondary communicating sensing slots also reduced with the SIS factors owing to the increase in temporal and spatial false alarms. The results validate the existence of different spectral opportunities in CR-IoT spectrum-heterogeneous environment by demonstrating different average number of communicating sensing slots for \(R1\) and \(R2\). Importantly, the average number of successful secondary communicating sensing slots for FD-SU TX in \(R1\) was less than that of \(R2\).