Auto-tuning and Self-optimization of 3G and Beyond 3G Mobile Networks. (Paramétrage Dynamique et Optimisation Automatique des Réseaux Mobiles 3G et 3G+)

With the wireless mobile communication boom, auto-tuning and self-optimization of network parameters are more than ever key issues to provide high-quality services for the end-user and to decrease the operational expenditure of the network operation. The special attention drawn to the self-optimization of radio resource management (RRM) parameters is motivated by the user need for ubiquitous communication and by the increasing complexity of networks resulting from the cooperation of radio access technologies. Formerly, RRM has been based on some algorithms (admission control, resource allocation, handover .) governed by a set of fixed thresholds. Today, RRM procedures have undergone a considerable change and the paradigm will shift towards a completely automatic network management. Optimal auto-tuning mechanisms, performed by control methods such as fuzzy logic optimized by reinforcement learning, could considerably improve network management functions with respect to traditional RRM algorithms with fixed parameters. This thesis introduces new results in auto-tuning and self-optimization of RRM parameters in 3G and beyond 3G networks. Auto-tuning tasks are organized in the control plane where different information exchange is involved between the network nodes. Auto-tuning using fuzzy logic control is performed in a local loop, namely the controller is in continuous interaction with the network. The controller feeds the network with new parameter settings and conversely the network returns its feedback by delivering new quality indicators indicating its operating state. Different use-cases are investigated. First, an auto-tuning of resource allocation algorithm in UMTS is studied as an alternative to the existing static resource allocation. The auto-tuning process dynamically adapts a guard band that is reserved for users using real time services. A best trade-off between real time and non real time services is achieved in the sense that the quality of service becomes comparable in the two traffic classes especially in a high load situation. The second use-case concerns the self-optimization of soft handover parameters in UMTS networks. For each cell the controller receives as inputs the filtered downlink load and that of its neighbouring cells. The controller continually learns the best parameter values in each network situation. The learning process is governed by a utility function. Simulation results reveal significant improvements in terms of network performance. The proposed auto-tuning algorithm balances the radio load between base stations and improves the system capacity by up to 30% compared to a UMTS network with fixed soft handover parameters. However, the auto-tuning increases the signalling messages load in the radio interface as well as in the core network. This negative effect is minimized by reducing the reactivity of the auto-tuning controller. The third case deals with the auto-tuning of LTE (3GPP Long Term Evolution) mobility algorithm. The auto-tuning is carried out by adapting handover margin involving each couple of cells according to the difference between their loads. The auto-tuning alleviates cell congestion and balances the traffic and the load between cells by handing off mobiles close to the cell border from the congested cell to its neighbouring cells. Simulation results, based on simplified system and interference models, show that the auto-tuning process brings about an important gain in both call admission rate and user throughput. Finally, an algorithm of intersystem mobility between UMTS and WLAN access technologies is proposed. The algorithm is coverage and load based, and is governed by two thresholds: the first is responsible for handover from UMTS to WLAN and the second - for the inverse direction. The self-optimization of the two thresholds is jointly performed. The results obtained using the adaptive intersystem mobility algorithm show high tracking capacity gain, and illustrate the importance of intelligent cooperation between technologies. The results provided in this thesis are supported by theoretical analysis and extensive dynamic system level simulations with multi-cells' scenarios, including the effect of many relevant mechanisms that have an impact on the radio access. However, simulations do not exactly reflect the reality in the network operation. So auto-tuning should be tested in a real experimental network (test-bed).