5.5 Applications to Performance Analysis in IEEE 802.22
5.6.4 Performance Comparison between Different Channel
Now we compare the extended data delivery time of the following three schemes: (1) the slot-based target channel selection scheme; (2) the random-based target channel selection scheme; and (3) the traffic-adaptive target channel selection scheme. We consider a three-channel network with various
6ρp= λpE[Xp] and ρs= λsE[Xs].
0 0.2 0.4 0.6 0.8 1 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Normalized traffic workload of the primary connections (ρ
p
)
Normalized traffic workload of the secondary connections (ρ s)Non−feasible Region
Admissible Region
Figure 5.7: Admissible region for the normalized traffic workloads (ρp, ρs), where the average cumulative delay constraint can be satisfied when ts = 0 (slot), E[Xp] = 20 (slots/arrival) and E[Xs] = 10 (slots/arrival).
traffic loads, where λ(1)p = λ(2)p = λ(3)p ≡ λp, λ(1)s = λ(2)s = λ(3)s ≡ 0.01 (arrivals/slot), (E[Xp(1)], E[Xp(2)], E[Xp(3)]) = (5, 15, 25) (slots/arrival), and (E[Xs(1)], E[Xs(2)], E[Xs(3)]) = (15, 15, 15) (slots/arrival). For the slot-based scheme, the secondary connections prefer selecting the channel which has the lowest busy probability resulting from the primary connections in each time slot. That is, when handoff procedures are initiated in the beginning of each time slot, all the secondary connections will select channel 1 to be their target channels. Furthermore, the random-based scheme selects one channel out of all the three channels for the target channel. Hence, each channel is selected with probability 1/3. Moreover, based on the considered traffic parameters, the traffic-adaptive scheme will adopt the always-changing se-quence and the always-staying sese-quence when λp ≤ 0.018 (arrivals/slot) and λp ≥ 0.018 (arrivals/slot), respectively. The three target channel selection schemes result in various target channel sequences. Based on the proposed analytical model, we can evaluate the average extended data delivery time resulting from these target channel sequences.
Figure 5.8 compares the extended data delivery time of the three target channel selection methods. We have the following three important obser-vations. First, we consider λp < 0.018 (arrivals/slot). Because the proba-bility of changing operating channel is higher than that of staying on the current operating channel for the interrupted secondary user in the random-based scheme, we can find that the average extended data delivery time for the random-based target channel selection scheme is similar to that for the traffic-adaptive target channel selection scheme, which adopts the always-changing sequence. Secondly, when λp > 0.018 (arrivals/slot), the traffic-adaptive scheme can shorten the average extended data delivery time be-cause it adopts the always-staying sequence. For a larger value of λp, the
0.015 0.016 0.017 0.018 0.019 0.02 0.021 0.022 18
20 22 24 26 28 30
Arrival rate of the primary connections (λ
p)
Average extended data delivery time (E[T]) (Unit: slots)Slot−based Scheme Random−based Scheme Traffic−adaptive Scheme
Figure 5.8: Comparison of average extended data delivery time for different target channel selection sequences.
traffic-adaptive scheme can improve the extended data delivery time more significantly. Thirdly, it is shown that the random-based and traffic-adaptive schemes can result in shorter extended data delivery time compared to the slot-based scheme. For example, when λp = 0.018, the random-based and traffic-adaptive schemes can improve the extended data delivery time by 35%
compared to the slot-based scheme. This is because the slot-based scheme ignores the queueing behaviors of the secondary connections.
Chapter 6
Optimal Proactive Spectrum Handoff
Extended to the discussions of the proactive spectrum handoff in Chapter 5, we further investigate how to predetermine the optimal target channel sequence for future handoffs. We incorporate two important features in the design of spectrum handoff to ensure the quality of service (QoS) for the secondary users. First, due to multiple interruptions from the primary users in each secondary user’s connection, a series of spectrum handoffs are consid-ered in our model. Secondly, we consider the impacts of the traffic statistics of both the primary and secondary users on the handoff delay.
In this chapter, we formulate an optimization problem of finding a target channel sequence for multiple handoffs with the objective of minimizing the cumulative delay per connection for a newly arriving secondary user. We will simultaneously consider two design features in spectrum handoffs: (1) multiple spectrum handoffs and (2) various service time of the primary and secondary users. The contributions of this chapter can be summarized in the following:
• We propose a dynamic-programming-based algorithm with time com-plexity of O(LM2) to find an optimal target channel sequence with minimum cumulative spectrum handoff delay, where L and M are the length of the target channel sequence and the total number of candidate channels for spectrum handoffs, respectively.
• Furthermore, a low-complexity greedy algorithm is proposed to find the suboptimal solution with time complexity of O(M). We prove that only six permutations of the target channel sequences are required to be compared, and demonstrate that it can approach the optimal solution.
6.1 Problem Formulation
The extended data delivery time is an important QoS performance metric for secondary users from a connection viewpoint. The extended data deliv-ery time per connection consists of the service time of one connection and the cumulative handoff delay resulting from multiple handoffs. Because the cumulative handoff delay depends on which channels are selected when the primary users’ interruptions occur, one of important issue for the secondary users is to search the best target channel sequence.
We consider a CR network G with M independent channels, where the target channel sequence for future spectrum handoffs is determined proac-tively for each newly arriving secondary user. For a secondary user with default channel s0 , η, we denote its target channel sequence as s(η) , (s1, s2, s3, · · · ) where si,η is the target channel for spectrum handoff at the ith interruption. Next, we formulate a Cumulative Handoff Delay Mini-mization Problem for multiple spectrum handoffs. Given a set of candidate channels Ω = {1, 2, . . . , M } and the required length L of the target channel
sequence for L spectrum handoffs, we aim to determine a target channel se-quence (denoted by s(η)∗) to minimize the average cumulative handoff delay E[D(s(η))] for a newly arriving secondary user’s connection. Formally, we have
s(η)∗ = arg min
∀s(η)∈ΩL
E[D(s(η))] , (6.1)
where E[·] is the expectation function. In the next section, the closed-form expression for E[D(s(η))] will be derived given the arrival rates and service time distributions of both primary and secondary users.