Comparison between Different Spectrum Decision Schemes 75

Figure 4.11 shows the effects of λs on the average overall system time for three different channel selection schemes: (1) sensing-based method; (2) probability-based method; and (3) non-load-balancing method. Consider a three-channel system with the following traffic parameters: (λ⁽¹⁾p , λ⁽²⁾p , λ⁽³⁾p ) = (0.02, 0.02, 0.03), (E[Xp⁽¹⁾], E[Xp⁽²⁾], E[Xp⁽³⁾]) = (20, 25, 20), and E[Xs] = 10.

The overall system time of the probability-based and sensing-based channel selection schemes are calculated from (4.8) and (4.9), respectively. For the non-load-balancing method, all the secondary connections will select chan-nel 1 to be their operating chanchan-nels because chanchan-nel 1 has the lowest busy probability. One can find that both the load-balancing channel selection schemes can significantly reduce the average overall system time compared to the non-load-balancing scheme, especially for larger λ_s. When τ is small (e.g. 5 slots), the sensing-based spectrum decision scheme can result in the shortest overall system time. As τ increases, the improvement of the sensing-based spectrum decision over other schemes decreases. In addition, we also observe that when τ = 17 and λ_s < 0.026, the probability-based scheme has better overall system time performance than the sensing-based scheme.

This is because the probability-based spectrum decision scheme can select the channels with lower interrupted probability. By contrast, if λs > 0.026, the sensing-based scheme can result in shorter overall system time because the sensing-based scheme can significantly reduce waiting time through wide-band sensing. Based on (4.7), each secondary user can intelligently adopt the best channel selection scheme to minimize its overall system time. The two considered load-balancing spectrum decision methods can reduce the over-all system time by over 50% compared to the existing non-load-balancing

0.01 0.015 0.02 0.025 0.03 0.035 0.04 40

60 80 100 120 140 160

Average Arrival Rate of the Secondary Connections (

)

Overall System Time (E[S])

Non−load−balancing Method Probability−based Method Sensing−based Method (

= 17) Sensing−based Method (τ = 5)

Figure 4.11: Comparison of the overall system time for three considered spectrum decision schemes, where PF = 0.1, PM = 0.1, and E[Xs] = 10.

method when λ_s = 0.04.

Chapter 5

Proactive Spectrum Handoff

Spectrum handoff mechanisms can be generally categorized into two kinds ac-cording to the decision timing of selecting target channels [85]. The first kind is called the proactive spectrum handoff¹, which decides the target channels for future spectrum handoffs based on the long-term traffic statistics before data connection is established [72, 90, 91]. The second kind is called the re-active spectrum handoff scheme [92]. For this scheme, the target channel is searched in an on-demand manner [93, 94]. After a spectrum handoff is re-quested, spectrum sensing is performed to help the secondary users find idle channels to resume their unfinished data transmission. Both spectrum hand-off schemes have their own advantages and disadvantages. A quantitative comparison of the two spectrum handoff schemes was provided in [95].

In this chapter, we focus on the modeling technique and performance

1In this dissertation, we assume that spectrum handoff request is initiated only when the primary user appears as discussed in the IEEE 802.22 wireless regional area networks (WRAN) standard. In this scheme, the proactive spectrum handoff represents the spec-trum handoff scheme with the proactively designed target channel sequences. It is different from the proactive spectrum handoff in [29, 34–42] that assumes spectrum handoff can be performed before the appearance of the primary users.

analysis for the proactive spectrum handoff scheme, while leave the related studies on the reactive spectrum handoff in Chapter 6. Compared to the reactive spectrum handoff scheme, the proactive spectrum handoff is eas-ier to achieve a consensus on their target channels between the transmit-ter and its intended receiver because both the transmittransmit-ter and receiver can know their target channel sequence for future spectrum handoffs before data transmission. Furthermore, the change switching delay of the proactive spec-trum handoff is shorter than that of the reactive specspec-trum handoff because scanning wide spectrum to determine the target channel is not necessary at the moment of link transition. Nevertheless, the proactive spectrum handoff scheme shall resolve the obsolescent channel issue because the predetermined target channel may not be available any more when a spectrum handoff is requested.

The contribution of this chapter is to propose a preemptive resume pri-ority (PRP) M/G/1 queueing network model to characterize the spectrum usage behaviors of the connection-based multiple-channel spectrum hand-offs. Based on the proposed model, we derive the closed-form expression for the extended data delivery time of different proactively designed target channel sequences under various traffic arrival rates and service time distri-butions. We apply the developed analytical method to analyze the latency performance of spectrum handoffs based on the target channel sequences specified in the IEEE 802.22 wireless regional area networks (WRAN). We also suggest a traffic-adaptive target channel selection principle for spectrum handoffs under different traffic conditions.

5.1 Motivation

To characterize the channel obsolescence effects and the spectrum usage be-haviors with a series of interruptions in the secondary connections, we suggest a new performance metric - the extended data delivery time of the secondary connections. It is defined as the duration from the instant of starting trans-mitting data until the instant of finishing the whole connection, during which multiple interruptions from the primary users may occur. In the context of the connection-based spectrum handoffs, how to analyze the extended data delivery time is challenging because three key design features must be taken into account: (1) generally distributed service time, where the probability density functions (pdfs) of service time of the primary and secondary con-nections can be any distributions; (2) different operating channels before and after spectrum handoff; and (3) queueing delay due to channel contention from multiple secondary connections. To the best of our knowledge, an ana-lytical model for characterizing all these three features for multiple handoffs has rarely been seen in the literature.

5.2 System Model

5.2.1 Assumptions

In this chapter, we make the following assumptions:

• A default channel is preassigned to each secondary user through spec-trum decision algorithms in order to balance the overall traffic loads of the secondary users to all the channels [79]. When a secondary trans-mitter has data, it can transmit handshaking signal at the default chan-nel of the intended receiver to establish a secondary connection [96].

If the corresponding receiver’s default channel is busy, the secondary transmitter must wait at this channel until it becomes available [34].

• Each primary connection is assigned with a default or licensed channel.

• Each secondary user can detect the presence of the primary user. In fact, this model can be also extended to consider the effects of false alarm and missed detection as discussed in Chapter 7.

• Any time only one user can transmit data at one channel.

5.2.2 Illustrative Example of Proactive Multiple