The Performance of the Factor k

At last, the tunable factor k of suspend algorithm in equation (3-5) is evaluated in Figure 5-6. The average number of the handed-down GSM RSSI samples and the variance are illustrated in the figure. The factor k is the weighting factor about the influence of RSCP ratio.

Figure5- 6. The number of hand-down RSSI samples with different k (User = 45)

The increasing k will force the suspension goes to users who are close to the base station easily and make it harder to suspend users who are at the hand-down region. As shown in Figure 5-6, the number of RSSI measurement samples before GSM hand-down will increase

as the k increases. In other respect, high k makes the distance (or the associate RF) have bigger impacts on the selection of the suspend users. As a result, in a mobility environment, the variance of the RSSI measurement will also increase for higher k. The factor k trades off between measurement efficiency and fairness.

As shown in Figure 5-7, the relationship of the suspend probability and the strength of RSCP is illustrated. The suspend probability between the stop threshold (-104 dBW) and the start threshold (-108 dBW) is relative small. Besides this region, the larger RSCP (i.e. the RF condition is better) has higher opportunity to be suspended than the lower RSCP. With the modified factor k increasing, the RF condition is more dominated and this phenomenon is becoming obvious. It can explain in Figure 18 why the number of the hand-down samples is higher when the k is larger. The factor k can easily modify the importance of the RF influence.

Figure5- 7. The relationship of the suspend probability and RSCP with different k (User = 45)

Chapter 6 Conclusions

6.1 Contributions

In this thesis, the capacity-based compressed mode is proposed to resolve the excessive power increase during the compressed mode. To differentiate the priority users based on RF condition, number of RSSI measurement, and continuous suspension avoidance, the proposed algorithm can effectively reserve the capacity by reducing the number of simultaneous compressed mode measurements while keeping the more-than-enough measurements before a user handing-down to GSM system. Furthermore, with different choices of k factors, users with different priority in terms of the necessity of border-cell hand-down can be easily separated. The proposed capacity-based compressed mode control can apply to a general design concept for trading off the capacity and handover performance if users’ priority can be identified. The contributions of this thesis are summarized as:

1. Study and report on the detail operations of the compressed mode to execute inter-system handover.

2. The proposed capacity-based compressed mode improves the performance impacts on the capacity caused by the compressed mode.

3. The simulation platform is established for both intra-system handover and inter-system handover in UMTS system.

4. The simulation platform verifies the proposed control algorithm truly works and it can be applied to handover to other systems.

6.2 Future Works

In this thesis, the simulation only considers about the same 12.2 kbps circuited switched services. In the future, the different QoS services are occurred and it will challenge this algorithm. Since the proposed capacity-based compressed mode only considers the measurement issue. For making handover decisions, no matter what services needs the enough carriers’ information of other system. Thus, the algorithm is still work but it will add further criteria in the handover stages. Like the ideas in [33], the handover ordering is decided by the signal strength and the service profile. The next step, this algorithm can consider about the multimedia services and add new control criteria to inter-system handover.

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