Pago efectivo de la multa.

During a triggering of SHO algorithm, different signalling procedures are involved [95]. The signalling messages for an active set update (link addition or deletion) are mainly transported in the DCCH (Dedicated Control Channel). The DCCH transports the RRC layer (Radio Resource Control) messages for SHO procedure such as measurement report and active set update. The quality of this logical channel is very important for the success of SHO procedures.

The channels DCCH and DTCH (Dedicated Traffic Channel) are mapped into DCH (Dedicated Channel) and SRB (Signalling Radio Bearer) respectively [19]. Sometimes, the SRB is a part of the DCH and its bit rate is usually fixed to 3.4kbit/s. For a call, the RRC signalling traffic is low compared to DTCH traffic but a high number of active set update generates more traffic in the

DCCH. So the question that can be arisen here is whether the auto-tuning influences the capacity in the signalling channel and the stability of the system.

Figure 4.14 shows the impact of the controller on the distribution of Ping-Pong effect. The Ping- Pong effect is related to the frequency of active set update since whenever a mobile add or delete a link in the active set, some signalling messages are involved in the radio and core interfaces. Here the frequency of the active set update is measured as the number of active set updates, generated by each mobile, over its sojourn time in the network. In order to give prominence to this effect, we simulate two network environments: low mobility (speed =3km/h for 20% of users) and high mobility (speed =60km/h for 20% of users) environment. When the controller performs a regulation at every 50s in a low-mobility environment, the frequency of active set update is increased by 10% for more than 10% of mobiles compared to a classical network. So the proposed controlling process increases the signalling messages by 10%. This ratio goes up to 14.3% in a high-mobility environment. By decreasing the reactivity of the controller to one regulation per 100s, the additional signalling introduced by the controller is decreased to 7.1% in a high-mobility environment. The adaptation of the controller or the auto-tuning reactivity needs to be carefully studied in each network environment. The trade-off between the capacity gain and the associate signalling introduced by the auto-tuning is out of scope.

45 50 55 60 65 70 75 80 85 90 95 100 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 0,50

Frequency of Active Set update (1/s)

C D F ( % )

Classic network with mobilty=3km/h for 20% of users

Optimized network with mobilty=3km/h for 20% of users and reactivity=50s Classic network with mobilty=60km/h for 20% of users

Optimized network with mobilty=60km/h for 20% of users and reactivity=50s Optimized network with mobility=60km/h for 20% of users and reactivity=100s

4.5

This chapter has presented results for the application of auto-tuning and optimization algorithms in UMTS networks. The following scenarios have been studied:

The first case is the auto-tuning of resource allocation: the RT guard band is dynamically regulated to achieve optimal tradeoffs between QoS of RT and NRT users. Simulation results have shown an efficient compromise between the perceived QoS in RT and NRT services especially when the traffic is unbalanced. However, when the traffic of both services is very high, the auto-tuning does not improve the QoS for both services.

The second scenario is the mobility auto-tuning: The auto-tuning algorithm dynamically changes the SHO parameters setting as a function of traffic condition, and implicitly performs traffic balancing between cells. This last case study shows an important gain in the overall network performances. The global capacity gains brought by the auto-tuning of SHO parameters are important and typically reach 30% compared to a network with fixed parameters. We have shown that the auto-tuning increases the frequency of active set updates and then increases the signalling messages in the radio interface as well as in the core network. The Ping-Pong phenomena can be reduced by bringing down the reactivity of the auto-tuning. So the auto-tuning should be applied to a network with a special care since it impacts the stability of the system. The study on auto-tuning of mobility parameters in UMTS network is currently investigated in the long term evolution (LTE) of UMTS in 3GPP TSG-RAN WG3. The next chapter deals with the LTE mobility auto-tuning. Instead of using fuzzy reinforcement learning for the auto-tuning, the LTE mobility adaptation is based on a predefined auto-tuning function.

5

Chap. 5

Self optimization of mobility algorithm in

LTE networks

5.1

3GPP (3rd Generation Partnership Project) organization has defined the requirements for an evolved UTRAN (e-UTRAN: UMTS Terrestrial Radio Access Network) [96] and are currently in advanced process of its specification [97]. The evolution of 3G UTRAN is referred to as the 3GPP Long Term Evolution (LTE). Different working groups are involved in defining the architecture and the technology of the radio access and the core network [98]. In the framework of the working group 3GPP TSG-RAN WG3, there have been discussions and studies on the use of self-configuration, self-tuning and self-optimization in the e-UTRAN system [99]. In the first phase of the network optimization/adaptation, neighbour cell list optimization and coverage and capacity control have been proposed [99]. The study of auto-tuning of mobility parameters has been further identified as a relevant case study of self-configuration and has been proposed in different technical reports [100] [101].

The purpose of this chapter is to present an approach for auto-tuning of handover algorithm in LTE system and to present through a case study the performance achieved by the proposed auto- adaptation approach. Unlike in UTRAN where soft-handover is used for mobility, in e-UTRAN a hard handover solution for mobility has been adopted. The handover algorithm has not been specified by 3GPP for the e-UTRAN and for this reason, we adapt an algorithm similar to the one used in GSM networks.

The chapter is organized as follows: in the second section, an overview on the LTE system is presented, including the system requirements, architecture and the physical layer. The third section develops system and interference models. The fourth section deals with the LTE mobility management algorithms including its auto-tuning scheme. Simulation results are given in the fifth section. Finally, a conclusion summarizes the chapter.