Performance Evaluation
4.2 Simulation Results
4.2.3 Performance Validation and Comparison under Gener- Gener-alized CR Networks
4.2.3.1 Performance Validation of Proposed OCS Approach
Figs. 4.4(a) and 4.4(b) shows the performance validation between simulation and an-alytical results by adopting the proposed OCS scheme under the generalized CR net-works. Considering different arrival rates λCR of the CRUs, the sensing threshold is selected as pd = 0.93 and the number of channels is M = 4 with PU’s arrival rate at each channel as λi = 0.1, 0.1, 0.3, 0.3 for i = 1, 2, 3, 4. It can be observed from both figures that the analytical results can match with the simulation results under all the different cases. As in Fig. 4.4(a), the aggregate throughput increases with the number of CRUs and decrease at higher numbers of CRUs due to the insufficient channel avail-ability. Lowered aggregate throughput is observed at high CR traffic, e.g., at λCR = 1, due to the decreasing rendezvous probability pv,i. The CR transmitters do not have much chance to meet with their corresponding CR receivers since those receivers may also conduct data transmission in another channel. Therefore, from the aggregate frame delay in the primary network as in Fig. 4.4(b), the delay will increase with the aug-mentation of the CR traffic. Under the same CR traffic, delay only slightly change
0 10 20 30 40 50 60 70 80 90
(a) Aggregate throughput vs number of CRUs.
0 10 20 30 40 50 60 70 80 90
(b) Aggregate frame delay vs number of CRPs.
Figure 4.4: Performance validation of OCS scheme under pd = 0.93 and number of channels M = 4 with PU’s arrival rate at each channel as λi = 0.1, 0.1, 0.3, 0.3 for i = 1, 2, 3, 4.
after exceed some number of CRUs, which means that there is no use in putting the CRUs into channels except virtual channel due to the decrease in channel availability.
In other words, under the same CR traffic, OCS decreases the channel availability for probability of rendezvous before reaching the number of CRUs which makes the best trade-off between channel availability and probability of rendezvous. After that, the aggregate throughput will degrade caused by the decreasing probability of rendezvous because there is no gain from putting more CRUs into channels except virtual channel.
As for the light traffic, CRUs can’t utilize the channel very well with the small num-ber of CRUs in transmitting group at first. However, after the numnum-ber of CRUs is large enough to explore the channel availability, the aggregate throughput in light traffic will transcend any other heavy traffic scenarios. Therefore, from the discussion in gener-alized condition with OCS, the logic partition problem accounts for the degradation in aggregate throughput especially in heavy traffic CR network. Moreover, under the light CR traffic, it will have better approximation between simulation and theoretical results, because the small channel-hopping probability resulted from high rendezvous rate will lead to small variance in binomial distribution in approximation.
4.2.3.2 Performance Comparison
As for the generalized CR network, Fig. 4.5 shows the aggregate throughput of CRUs and its corresponding delay of PU in Fig. 4.6. As can be seen, light traffic with high rate of rendezvous, thus, the OCS will work well by allocating the few CR transmitters into better channel to increase the channel utilization. When the CR traffic is getting high as 0.4, there is no much difference between these three selection methods because the number of CR transmitters and rate of rendezvous are just proper to explore the channel utilization. While the CR traffic is as high as 0.7, OCS can work well again compared to other selection methods due to the low rate of rendezvous in this scenario.
10 20 30 40
Figure 4.5: Aggregate throughput of CRUs under different channel-hopping sequences with pd = 0.93 and number of channels M = 4 (each channel has arrival rate λi =
Figure 4.6: Aggregate frame delay of PUs under different channel-hopping sequence with pd = 0.93 and number of channels M = 4 (each channel has arrival rate λi = 0.1, 0.1, 0.3, 0.3 for i = 1, 2, 3, 4).
0 20 40 60 80
Figure 4.7: Simulation for Comparison between proposed algorithms in pd = 0.93 and W = 64 with λCR = 1 (dashed) and λCR = 0.6 (solid) under OCS, OCS-WSC and OCS-WCSC (M, ◦, and ¤ curves, respectively) and number of channels M = 4 (each channel has arrival rate λi = 0.1, 0.1, 0.3, 0.3 for i = 1, 2, 3, 4).
In other words, OCS can allocate the CRUs to gather to some channels to increase the rate of rendezvous. As for aggregate frame delay of PUs, the percentage in difference in delay between these three methods is still consistent to the result as in aggregate throughput.
4.2.3.3 Enhancement with WSC and WCSC Mechanisms
In Fig. 4.7, 4.8, and 4.9, the comparison between OCS, OCS-WSC, and OCS-WCSC are provided w.r.t. different system parameters in generalized CR network such as the probability of detection pd and contention window size W . It is noted that although there should be a regulation for pd in QoS of delay prescribed by the primary network in [23; 26], Pd can still be determined by the CRUs themselves due to the control in hopping sequence. In other words, the QoS in primary network can be promised by the admission control in CRUs.
First, as the algorithms described in chapter 3.2 and 3.3, WSC and
OCS-0 20 40 60 80
Figure 4.8: Simulation for Comparison between proposed algorithms in pd = 0.93 and W = 16 with λCR = 1 (dashed) and λCR = 0.6 (solid) under OCS, OCS-WSC and OCS-WCSC (M, ◦, and ¤ curves, respectively) and number of channels M = 4 (each channel has arrival rate λi = 0.1, 0.1, 0.3, 0.3 for i = 1, 2, 3, 4).
Figure 4.9: Simulation for Comparison between proposed algorithms in pd = 0.96 and W = 16 with λCR = 1 (dashed) and λCR = 0.6 (solid) under OCS, OCS-WSC and OCS-WCSC (M, ◦, and ¤ curves, respectively) and number of channels M = 4 (each channel has arrival rate λi = 0.1, 0.1, 0.3, 0.3 for i = 1, 2, 3, 4).
in probability of rendezvous between CRUs under the same collision probability with PUs, which can be seen from the aggregate frame delay of PUs in Fig. 4.7, 4.8, and 4.9. Fig. 4.8 and Fig. 4.9 show the comparison between OCS-WSC and OCS-WCSC in different pd. With the counter-reset mechanism in WCSC, it is intuitive that WCSC has much more opportunities in negotiation than WSC. However, with high pd, it will also induce the high collision probability between CRUs in WCSC due to more CRUs will be allocated by OCS to the channels except virtual channel for increasing channel utilization. With small number of CRUs in the network, there is no much difference between this two mechanisms due to low channel utilization. In other words, WSC and WCSC can’t work efficiently in the low traffic CR network obviously. As for the comparison between OCS-WSC and OCS-WCSC in different W , Fig. 4.7 and Fig. 4.8 show that small W will induce the high collision probability between CRUs in WCSC.
However, with large W , it will induce inefficiency for channel utilization. In general, WSC will dominate WCSC in throughput with high pd or small W at large number CRUs in the network.
As a result, the using range of the proposed OCS-WSC and OCS-WCSC schemes can consequently be recorded into a look-up table based on number of CRPs N, the CR traffic λCR, probability of detection pd, and contention window size W for real time implementation.
Chapter 5 Conclusion
In this thesis, under the considerations of imperfect spectrum sensing and synchroniza-tion, analytical models are developed for both the probability of channel availability and the average frame delay of multi-channel primary networks with the existence of cognitive radio (CR) users in paired and generalized networks. Based on the analysis, an approach for obtaining the optimal channel-hopping sequence (OCS) is designed based on the dynamic programming technique. The proposed OCS approach can both achieve maximum aggregate throughput of the CR users and ensure feasible average frame delay of primary users (PUs) under their quality of service (QoS) requirements.
Moreover, in order to address the logic partition problem occurs in the generalized CR network, the wake-up successive contention (WSC) and the wake-up counter-reset successive contention (WCSC) algorithms. By exploring a blind spot in the imperfect sensing and amending the conventional contention mechanisms, the proposed schemes can enhance the original OCS approach with increased number of negotiations in the CR networks. Both the analytical and simulation results show that the proposed OCS, WSC, and WCSC approaches can effectively enhance the aggregate throughput of the CR users and also guarantee the QoS requirements of the PUs.
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