Chapter 3 Dynamic Vertical Handover Control Algorithm
3.5 Summary of Proposed Vertical Handover Control Algorithm
≥ ∆
∆ ,
1 1
2 (3-15)
The handover timers are not fixed and will be updated based on the value r from time to time.
Figure 3-5. Trigger time functionality for downlink handover
3.5 Summary of Proposed Vertical Handover Control Algorithm
The proposed vertical handover control algorithm is composed by throughput-based mapping function, QoS-based dynamic handover threshold, and performance-based trigger timer. The procedures of the vertical handover control algorithm depicted in Figure 3-6:
1. Measures the signal strength and calculates the effective throughput from system perspective.
Time
∆ ∆
∆T ∆T
Throughput
0 R
UMTSR
WLAN Ping pong effect(compare throughput performance) handover
No handover
2. Uses the mapping function to get the effective SNR.
3. Applies different services with different QoS requirements to set the dynamic handover threshold and trigger timer for downlink and uplink handover.
4. Finally, trigger equations in Eq (3-1), (3-2) is used to make decision about the handover.
Measurement
Estimation (user perspective)
Calculation (system perspective)
Trigger timer
setting Mapping Threshold setting
Sw - Su > H for ∆Tdownlink Sw–Su < H for ∆Tuplink
services
SNR
R
RE
∆Tuplink,
∆Tdownlink SW, SU H
Yes, Handover
No, stay
Figure 3-6. The procedure of proposed vertical handover algorithm
Chapter 4
Mathematical Model and Numerical Analysis
In this chapter, a mathematical model is created to analyze the handover performance.
Simulation results and the analysis for the proposed vertical handover algorithm are also given
4.1 Mathematical Model
In this section, the performance of the handover frequency and average throughput is analyzed and the impacts from the path loss and shadow fading are considered. The fast fading will be ignored due to the averaging of the measurements. As calculated in Eq. (4-1) and (4-2), the signals (in dB) received at MS from UMTS and WLAN, are U (k) and W (k), respectively.
( ) ( )
dk u dkK K k
U( )= 1− 2log + (4-1)
( )
(
w dk)
v(
w( )
dk)
K K k
W( )= 3− 4log + (4-2)
As shown in Figure 4-1, dk is the distance when the MS is d meters from UMTS at kth sample time and the functionw (dk) calculates the distance from WLAN when the mobile is d meters
from UMTS. K1, K2, K3, and K4 are parameters for the path loss. The shadow fading, u (dk) and v (dk) are assumed to be independent and identically distributed stationary Gaussian random processes with zero mean and variance,σ . 2
Figure 4-1. UMTS and WLAN location
The received signal will be averaged by applying an exponential filter, implemented as a low-pass filter:
model with associated probabilities will be used for representing the behavior of the vertical handover. As derived in Eq. (4-5), the state probabilities of UMTS, (PUMTS), WLAN, (PWLAN) and handover probability, (Pho(k)), can be calculated based on both the transition probabilities of UMTS to WLAN, (Pw|u), and WLAN to UMTS, (Pu|w).
Figure 4-2. Handover probability motion
Assuming that MS first connects to UMTS where PUMTS (0) =1, PWLAN (0) =0, it can be seen that Eq. (4-5) can be solved if the probability Pw|u(k) and Pu|w(k) are known.
To solve the transition probability, a vertical handover process is depicted in Figure 4-3.
From Figure 4-3, in UMTS-to-WLAN downlink handover, the transition will occur at the Kth
interval if SWLAN(Kdownlink)−SUMTS(Kdownlink)>H(Kdownlink) for ∆Tdownlink intervals. Here, if each mapping sample is independent, Pw|u(dk) can be written as:
)}
Figure 4-3. Finite state machines for handover process
Eq. (4-6) can be also rewritten as
which assumes that the current state and the received strength between the UMTS and WLAN are independent. Similarly, for WLAN-to-UMTS uplink handover, the probability Pw|u(dk) can also be written as: substituting Eq. (4-11) into (4-8) and (4-9), we can then calculate Pw|u(k) and Pu|w(k). Finally, from Eq. (4-5), Pho(k), Pu(k), and Pw(k) can be derived.
When the vertical handover probability Pho(k) and the state probabilities Pu(k) and Pw(k) are calculated, the vertical handover frequency,H , and average throughput, R , for a single f
user can be calculated as follows:
T this model and give discussions.
4.2 Numerical Results and Analysis
With the defined scenario, the performance derived in Eq. (4-5) can then be used to verify the proposed vertical handover control algorithm. Without losing the generality, a hot-spot scenario is assumed where there is only one UMTS base station and one WLAN access point. As shown in Figure 4-1, the trajectory of mobile will across the WLAN coverage.
The system and traffic parameters are listed in Table 4-1:
Table 4-1. Parameters Used for Numerical Analysis
Parameter Value
UMTS radius 500 meter
WLAN radius 50 meter
Separation between UMTS and WLAN 250 meter
K1,K3 (path loss parameter) 0dB
K2,K4 (path loss parameter) 30dB
σ (shadowing variance) 2 6 dB Tav (average distance for filter) 30 ms
∆ (handover latency) 500 ms
Sample time 50 ms
Video traffic delay bound (DB) 50 ms
Video frame per second (1/PI) 25 fps
Allowable frame loss rate (B) 5%
α (parameter in H) 5 dB
In this simulation, both non-real-time services and real-time services are considered. A baseline vertical handover algorithm (based on the signal-strength trigger only) is used as the reference to quantify the proposed vertical handover control algorithm.
4.2.1 Non-real-time services
As discussed, for non-real-time services, achieving higher transmission rates will be the major focus of the proposed vertical handover algorithm. As shown in Figure 4-4, for non-real-time services, the proposed vertical handover control algorithm achieves higher system throughput than the baseline handover control algorithm. As expected from Figure 4-5, the chance of staying in WLAN is higher in the proposed vertical handover control algorithm.
Also, as depicted in Figure 4-6, the proposed vertical handover control algorithm can substantially reduce the handover frequency, which has a positive impact on the processing power and the over-the-air signaling.
0 10 20 30 40 50 60 70 80 90 100 1
2 3 4 5 6 7 8 9
Time (s )
Throughput (Mbps)
proposed
baseline (signal-strength)
Figure 4-4. Throughput statistics for non-real-time services
Figure 4-5. Average state probabilities for different conditions
1 2
Figure 4-6. Performance comparisons for non-real-time services
4.2.2 Real-time services handover frequency. In this case, the proposed algorithm keeps the mobile in the UMTS about 60% of the time when the user initially connected to UMTS, shown in Figure 4-5.
0 10 20 30 40 50 60 70 80 90 100
Figure 4-7. Frame loss statistics (error-free channel) for real-time services
1 2
Figure 4-8. Performance comparisons for real time services
Chapter 5
Conclusions
5.1 Contributions
The challenges in designing the vertical handover are addressed and corresponding solutions are proposed: (1) Throughput-based mapping function is used to resolve the no-common pilot problem in the integrated system. (2) QoS-based dynamic handover thresholds could dynamically change the handover criteria for real-time and non-real-time services. (3) Performance-based trigger timer will calculate the proper trigger timer to avoid excessive ping-pong effect caused by an unstable channel conditions. An analytic model is provided to analyze the handover performance in the heterogeneous networks. Finally, the results show the proposed algorithm could improve the transmission throughput for non-real-time services and could substantially reduce the packet loss rate for the real-time services by reducing the vertical handover frequency.
5.2 Future works
In designing the mapping function, it just considers the throughput performance as a mapping judgment. However the other issues like power budget, building cost, and etc should be also taken into considerations. So a cost function which is a combination of considered parameters with associated weighted factors is expected to be a new judgment for controlling
the vertical handover. Besides, in this scenario, one UMTS cell covers one WLAN cell is assumed. In fact, one UMTS cells would cover several WLAN cells due to the smaller coverage of WLAN system. So how to select the WLAN cell as a handover target is an urgent problem. This issue becomes difficult in uplink handover; this is because mobile can switch to original UMTS network or handover to other WLAN cells. Those two conditions might have different handover methods and performances, so how to choose the most suitable decision for satisfying users’ requirements is a future work. Finally, investigating how to apply this vertical handover control mechanism as a general form for other heterogeneous networks like WLAN/WMAN, Ultra-Wideband/UMTS is a good study in the future works.
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