In the following chapters, we will discuss various spectrum management tech-niques to demonstrate the effectiveness of this analytical model. For the spec-trum decision issue, we show how to determine which channels are required to probe and transmit. For the spectrum mobility issue, we illustrate how to characterize the effects of multiple handoffs, where the secondary users can have different operating channels before and after spectrum handoff. For the spectrum sharing issue, we explore how to determine the optimal admission probability to avoid the interference between primary and secondary users in the presence of false alarm and missed detection.
#2 on Ch2 #4 on Ch1
#2 on Ch1 #4 on Ch2
#1 on Ch1
#1 on Ch2 #2 on Ch1
#1 on Ch2 #2 on Ch1 #3 on Ch1 #4 on Ch2
#1 on Ch2 #2 on Ch2
#3 on Ch2
#5 on Ch1
1st 2nd 3rd
1st 2nd
2nd
1st
3rd 4th
1st
Interruption Event Occurs
#1 on Ch1
1st 2nd 3rd
#3 on Ch1
#3 on Ch1
Figure 3.3: Illustration of the physical meaning of random variable Φ(k)i . For example, Φ(1)2 is one of the third segments (gray areas) of the first, the third, and the fourth secondary connections.
Chapter 4
Load-Balancing Spectrum Decision
Spectrum decision is a crucial process in CR networks [13], which helps the secondary user select the best channel to transmit data from candidate chan-nels. In order to distribute the traffic loads of the secondary users evenly to these candidate channels, an effective spectrum decision scheme should take the traffic statistics of the primary users as well as the secondary users into account. In this chapter, we introduce a performance measure for evaluating various spectrum decision schemes – the overall system time of the secondary connection, which is defined as the duration from the instant that data arrives at system until the instant of finishing the whole transmission.
In this chapter, we investigate how to evaluate the overall system time for the sensing-based and the probability-based spectrum decision schemes in the CR network when multiple interruptions from the primary user and sensing errors are taken into account. To this end, we design our multiuser spectrum decision schemes on top of the preemptive resume priority (PRP) M/G/1 queueing model. Based on the proposed analysis-based framework,
we can design the suitable parameters to shorten the overall system time.
Unlike the non-load-balancing methods that multiple secondary users may contend for the same channel, the channel selection schemes based on the designed parameters of the proposed analytical model can evenly distribute the traffic loads of secondary users to multiple channels, thereby reducing the average overall system time. The major contributions of this chapter are summarized in the following:
• Derive the optimal selection probability for the probability-based chan-nel selection scheme.
• Develop a method to determine the optimal number of candidate chan-nels for the sensing-based channel selection scheme.
• Compare the sensing-based and the probability-based channel selection methods and suggest which spectrum decision scheme can result in shorter overall system time with various sensing error probabilities and traffic parameters.
• Characterize the effects of sensing errors on the spectrum decision schemes of CR networks in terms of the overall system time of the primary and the secondary connections.
4.1 Motivation
The overall system time of the secondary users’ connections is affected by the multiple interruptions from the primary users and the sensing errors like missed detection and false alarm for the primary users. Within the transmission period of the secondary users’ connection, it is likely to have multiple spectrum handoffs due to the interruptions from the primary users.
Clearly, multiple spectrum handoffs will increase the overall system time [71].
In the meanwhile, false alarm occurs when the detector mistakenly reports the presence of a primary user. In this situation, the overall system time of the secondary user’s connections becomes longer because the secondary users cannot transmit data even with an idle channel. When the detection of a primary user is missed, data collision of both the primary user and the secondary user occurs, resulting in retransmitting and prolonging the overall system time of the secondary users’ connections. Hence, it is crucial is incorporating the effects of multiple handoffs and the sensing errors of false alarm and missed detection in spectrum decision methods for CR networks.
In this chapter, two kinds of spectrum decision schemes are considered:
(1) the sensing-based spectrum decision scheme; and (2) the probability-based channel selection scheme. For the sensing-probability-based spectrum decision method, a secondary user selects its operating channel according to the in-stantaneous sensing results from scanning the wideband spectrum. For the probability-based spectrum decision method, the operating channel is se-lected based on the predetermined probabilities which are determined ac-cording to traffic statistics from the long-term observation. Note that the sensing outcomes in both the methods are related to the traffic statistics of both the primary users and the secondary users. The two considered spec-trum decision schemes have different design issues. For the sensing-based spectrum decision scheme, the total number of candidate channels for chan-nel selection significantly affects the overall system time because this scheme requires scanning all the candidate channels. Intuitively, a narrowband sens-ing (or a smaller number of candidate channels) can reduce the total senssens-ing time. However, it is difficult to find one idle channel from a small number of candidate channels. Hence, one challenge is to determine the optimal
num-ber of candidate channels to minimize the overall system time. On the other hand, the probability-based spectrum decision scheme needs to prevent the secondary users from selecting a busy channel. Hence, the most important issue is to determine the optimal channel selection probability to minimize the overall system time.